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Brain-derived neurotrophic factor (BDNF) is a potential biomarker of response to treatment in psychiatric disorders. As it plays a role in the pathophysiological development of schizophrenia and bipolar disorder, it is of interest to study its role in predicting therapeutic responses in both conditions. We carried out a systematic review of the literature, looking for differences in baseline BDNF levels and the Val66Met BDNF polymorphism in these disorders between responders and non-responders, and found information showing that the Val/Val genotype and higher baseline BDNF levels may be present in patients that respond successfully to pharmacological and non-pharmacological treatments. However, there is still limited evidence to support the role of the Val66Met polymorphism and baseline BDNF levels as predictors of treatment response. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) remains the leading cause of neurological disability among young adults worldwide, underscoring the urgent need to define the best therapeutic strategy. Recent advances in proteomics have deepened our understanding of treatment mechanisms and revealed promising biomarkers for predicting therapeutic outcomes. This study focuses on the identification of a protein profile of circulating extracellular vesicles (EVs) derived from neurons, oligodendrocytes, and B and T cells able to differentiate treatment responders and non-responders in 80 patients with MS. In the patients who responded to treatment, T cell-derived EVs were enriched in LV151, a protein involved in the promotion of anti-inflammatory cytokines, whereas Bcell-derived EVs showed elevated PSMD6 and PTPRC, related to immunoproteasome function. Oligodendrocyte- and neuron-derived EVs showed upregulated CO6A1 and COEA1, involved in extracellular matrix reorganisation, as well as LAMA5, NonO, SPNT, and NCAM, which are critical for brain repair. In contrast, non-responders showed higher levels of PSMD7 and PRS10 from B cell-derived EVs, associated with DNA damage, and increased levels of PERM and PERL from T cell-derived EVs, linked to nuclear factor kappa B activation and drug-resistant proteins such as HS90A and RASK. These findings highlight a distinct panel of proteins in EVs that could serve as an early indicator of treatment efficacy in MS. [ABSTRACT FROM AUTHOR]

Background: We aim to ascertain prognostic factors in the current management of anal cancer within this study. Methods: We reviewed the management and outcomes of anal cancer cases over a seven-year period, inclusive (2016–2023). The primary objectives were to assess the demographic characteristics, clinical presentation, and outcomes of all anal cancer patients within our institution. Kaplan–Meier survival analysis was used to estimate survival differences between cohorts, with statistical significance determined using log-rank testing. Cox proportional hazards regression was utilised to identify prognostic factors. Cox regression hazard ratios were reported along with confidence intervals and p-values. Results: The median follow-up time for the study was 29.8 months. Seventy-five patients with anal cancer were included in this study, with 88% (66/75) being squamous cell carcinoma (SCC) and the majority having regional disease (82.7% (62/75)). The median age at diagnosis was 63.4 years (36–94). There was a female preponderance (57.3% (43/75)). In total, 84% (63/75) underwent definitive chemoradiation (dCRT), with 7/63 (11.1%) requiring a salvage abdomino-perineal resection (APR) for residual or recurrent disease. Adverse prognostic indicators include those with T4 disease hazard ratio = 3.81, (95% CI 1.13–12.83, * p = 0.04), poorly differentiated tumour disease HR = 3.37, (95% CI 1.13–10.02, * p = 0.04), having N2 nodal status HR = 5.03, (95% CI 1.11–22.8, * p = 0.04), and having metastatic disease at diagnosis HR = 5.8, (95% CI 1.28–26.42, * p = 0.02). Conclusion: Presenting characteristics including stage, nodal, and differentiation status remain key prognostic indicators in those diagnosed with anal malignancy. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the continuous therapeutic efforts metastatic renal cell carcinoma (mRCC) is a dreadful disease, but the many options available provide an horizon of hope for these patients. Sequential therapy based on vascular endothelial growth factor-tyrosine kinase inhibitors (VEGFR-TKI) continues in use. We present a nomogram for a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) mRCC treated with nephrectomy and VEGFR-TK, based on four independent clinical predictors: Eastern Cooperative Oncology Group (ECOG) status; International Metastatic RCC Database Consortium (IMDC) score; Morphology, Attenuation, Size and Structure (MASS) and Response Evaluation Criteria in Solid Tumors (RECIST) response criteria. This tool may be useful to clinicians assessing risk and prognosis of patients with mRCC. (1) Objective: To develop a clinically useful nomogram that may provide a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) metastatic renal cell carcinoma (mRCC) treated with nephrectomy and vascular endothelial growth factor receptor–tyrosine kinase inhibitor (VEGFR-TKI)-based sequential therapy. (2) Methods: A prospectively maintained database of 145 patients with mRCC treated between 2008 and 2018 was analyzed to predict the CSS of patients receiving sunitinib and second- and third-line therapies according to current standards of practice. A nomogram based on four independent clinical predictors (Eastern Cooperative Oncology Group status, International Metastatic RCC Database Consortium score, the Morphology, Attenuation, Size and Structure criteria and Response Evaluation Criteria in Solid Tumors response criteria) was calculated. The corresponding 1- to 10-year CSS probabilities were then determined from the nomogram. (3) Results: The median age was 60 years (95% CI 57.9–61.4). The disease was metastatic at diagnosis in 59 (40.7%), and 86 (59.3%) developed metastasis during follow-up. Patients were followed for a median 48 (IQR 72; 95% CI 56–75.7) months after first-line VEGFR-TKI initiation. The concordance probability estimator value for the nomogram is 0.778 ± 0.02 (mean ± SE). (4) Conclusions: A nomogram to predict CSS in patients with CC mRCC that incorporates patient status, clinical risk classification and response criteria to first-line VEGFR-TKI at 3 months is presented. This new tool may be useful to clinicians assessing the risk and prognosis of patients with mRCC. [ABSTRACT FROM AUTHOR]

Resistance to first-generation somatostatin receptor ligand (fgSRL) treatment in acromegaly is common, making the identification of biomarkers that predict fgSRL response a desired goal. We conducted a retrospective analysis on 21 patients with acromegaly who underwent surgery and subsequent pharmacological treatment. Through immunohistochemistry (IHC), we assessed the expression of the somatostatin receptor subtypes SSTR2 and SSTR5, E-Cadherin, and cytokeratin granulation pattern (sparsely or densely). Patients were divided into responders and non-responders based on their biochemical response to fgSRL and/or the newer agent, Pasireotide, or the GH-blocker, Pegvisomant. Patients resistant to fgSRL (n = 12) exhibited lower SSTR2 and E-Cadherin expressions. Sparsely granulated tumors were more frequent in the non-responder group. SSTR2 (p = 0.024, r = 0.49) and E-Cadherin (p = 0.009, r = 0.64) positively correlated with the Insulin-like Growth Factor 1 (IGF-1) decrease after fgSRL, while SSTR5 (p = 0.107, r = −0.37) showed a trend towards negative correlation. SSTR5 positivity seemed to be associated with Pasireotide response, albeit the number of treated patients was too low (n = 4). No IHC markers correlated with Pegvisomant response. Our findings suggest that densely granulated tumors, with positive SSTR2 and E-Cadherin seem to be associated with favorable fgSRL responses. The strongest predictive value of the studied markers was found for E-Cadherin, which seems to surpass even SSTR2. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored how genetic changes influence treatment effectiveness and survival in patients with advanced pancreatic cancer. By examining tumors from 142 patients, researchers identified specific genetic mutations that can predict how well a patient responds to chemotherapy. Findings showed that mutations in certain genetic pathways are associated with longer survival and better treatment outcomes. This research highlights the importance of tailoring cancer treatment based on individual genetic profiles, potentially leading to more personalized and effective therapies for pancreatic cancer patients. (1) Background: Pancreatic ductal adenocarcinoma (PDAC) has low survival rates despite treatment advancements. Aim: This study aims to show how molecular profiling could possibly guide personalized treatment strategies, which may help improve survival outcomes in patients with PDAC. (2) Materials and Methods: A retrospective analysis of 142 PDAC patients from a single academic center was conducted. Patients underwent chemotherapy and next-generation sequencing for molecular profiling. Key oncogenic pathways were identified using the Reactome pathway database. Survival analysis was performed using Kaplan–Meier curves and Cox Proportional Hazards Regression. (3) Results: Patients mainly received FOLFIRINOX (n = 62) or gemcitabine nab-paclitaxel (n = 62) as initial chemotherapy. The median OS was 13.6 months. Longer median OS was noted in patients with NOTCH (15 vs. 12.3 months, p = 0.007) and KIT pathway mutations (21.3 vs. 12.12 months, p = 0.04). Combinatorial pathway analysis indicated potential synergistic effects on survival. In the PFS, PI3K pathway (6.6 vs. 5.7 months, p = 0.03) and KIT pathway (10.3 vs. 6.2 months, p = 0.03) mutations correlated with improved PFS within the gemcitabine nab-paclitaxel subgroup. (4) Conclusions: Molecular profiling could play a role in PDAC for predicting outcomes and responses to therapies like FOLFIRINOX and gemcitabine nab-paclitaxel. Integrating genomic data into clinical decision-making can benefit PDAC treatment, though further validation is needed to fully utilize precision oncology in PDAC management. [ABSTRACT FROM AUTHOR]

The most promising treatment options for severe uncontrolled asthma (SUA) have emerged in recent years with the development of monoclonal antibodies for blocking selective targets responsible for the underlying inflammation, such as mepolizumab and benralizumab. However, there is variability in treatment response that is not fully controlled. The variability of the response to mepolizumab and benralizumab could be influenced by single-nucleotide polymorphisms (SNPs), and it would be useful to detect these and use them as predictive biomarkers of response. We conducted a retrospective observational cohort study of 72 Caucasian patients recruited from a tertiary hospital with severe uncontrolled eosinophilic asthma treated with mepolizumab and benralizumab. Polymorphisms in the IL5 (rs4143832, rs17690122), RAD50 (rs11739623, rs4705959), IL1RL1 (rs1420101, rs17026974, rs1921622), GATA2 (rs4857855), IKZF2 (rs12619285), FCGR2A (rs1801274), FCGR2B (rs3219018, rs1050501), FCGR3A (rs10127939, rs396991), FCER1A (rs2251746, rs2427837), FCER1B (rs1441586, rs573790, rs569108), and ZNF415 (rs1054485) genes were analyzed by real-time polymerase chain reaction (PCR) using Taqman probes. The response was analyzed after 12 months of treatment. In patients under mepolizumab treatment, a treatment response defined as a reduction in exacerbations was associated with ZNF415 rs1054485-T (p = 0.042; OR = 5.33; 95% CI = 1.06–30.02), treatment response defined as a reduction in oral corticosteroids use was associated with the number of exacerbations in the previous year (p = 0.029; OR = 3.89; 95% CI = 1.24–14.92), and treatment response defined as improvement in lung function was associated with the age at the beginning of biological therapy (p = 0.002; OR = 1.10; 95% CI = 1.04–1.18), FCER1B rs569108-AA (p < 0.001; OR = 171.06; 95% CI = 12.94–6264.11), and FCER1A rs2427837-A (p = 0.021; OR = 8.61; 95% CI = 1.71–76.62). On the other hand, in patients under benralizumab treatment, treatment response, defined as a reduction in exacerbations, was associated with ZNF415 rs1054485-T (p = 0.073; OR = 1.3 × 108; 95% CI = 1.8 × 10−19–NA), FCER1B rs569108-AA (p = 0.050; OR = 11.51; 95% CI = 1.19–269.78), allergies (p = 0.045; OR = 4.02; 95% CI = 1.05–16.74), and sex (p = 0.028; OR = 4.78; 95% CI = 1.22–20.63); and treatment response defined as improvement in lung function was associated with polyposis (p = 0.027; OR = 9.16; 95% CI = 1.58–91.4), IKZF2 rs12619285-AA (p = 0.019; OR = 9.1; 95% CI = 1.7–75.78), IL5 rs4143832-T (p = 0.017; OR = 11.1; 95% CI = 1.9–112.17), and FCER1B rs1441586-C (p = 0.045; OR = 7.81; 95% CI = 1.16–73.45). The results of this study show the potential influence of the studied polymorphisms on the response to mepolizumab and benralizumab and the clinical benefit that could be obtained by defining predictive biomarkers of treatment response. [ABSTRACT FROM AUTHOR]

Direct-acting antivirals (DAAs) revolutionized the therapeutics of chronic hepatitis C. The emergence and transmission of HCV variants with resistance-associated substitutions (RASs) can undermine HCV treatment. This study aimed to assess the prevalence and temporal trends of RASs in HCV, with a particular focus on clinically relevant RASs (cr-RASs). Near-complete HCV GenBank sequences archived in the Los Alamos HCV Database were analyzed. The study period was divided into two phases: before 2011 and from 2011 onward. Identification of RASs across three DAA classes (NS3, NS5A, and NS5B inhibitors) was based on the 2020 EASL guidelines. The AASLD-IDSA recommendations were used to identify cr-RASs for three HCV genotypes/subtypes (1a, 1b, and 3) and four DAA regimens: ledipasvir/sofosbuvir; elbasvir/grazoprevir; sofosbuvir/velpatasvir; and glecaprevir/pibrentasvir. The final HCV dataset comprised 3443 sequences, and the prevalence of RASs was 50.4%, 60.2%, and 25.3% in NS3, NS5A, and NS5B, respectively. In subtype 1a, resistance to ledipasvir/sofosbuvir was 32.8%, while resistance to elbasvir/grazoprevir was 33.0%. For genotype 3, resistance to sofosbuvir/velpatasvir and glecaprevir/pibrentasvir was 4.2% and 24.9%, respectively. A significant increase in cr-RASs was observed across the two study phases as follows: for ledipasvir/sofosbuvir in subtype 1a, cr-RASs increased from 30.2% to 35.8% (p = 0.019); for elbasvir/grazoprevir in subtype 1a, cr-RASs increased from 30.4% to 36.1% (p = 0.018); In subtype 1b, neither ledipasvir/sofosbuvir nor elbasvir/grazoprevir showed any cr-RASs in the first phase, but both were present at a prevalence of 6.5% in the second phase (p < 0.001); for sofosbuvir/velpatasvir in genotype 3, cr-RASs increased from 0.9% to 5.2% (p = 0.006); and for glecaprevir/pibrentasvir, cr-RASs increased from 12.0% to 29.1% (p < 0.001). The rising prevalence of HCV RASs and cr-RASs was discernible. This highlights the necessity for ongoing surveillance and adaptation of novel therapeutics to manage HCV resistance effectively. Updating the clinical guidelines and treatment regimens is recommended to counteract the evolving HCV resistance to DAAs. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the impact of CCR5 Δ32 and CTLA-4 polymorphisms on the response to IFN-β treatment in our cohort of MS patients from Croatia and Slovenia. Genomic DNA was obtained from 295 MS patients (230 female; 65 male) classified as responders (n = 173) and non-responders (n = 122) based on clinical criteria for treatment efficacy. Genotyping was performed via PCR/PCR-RFLP. No significant differences in the genotype/allele frequencies of CCR5Δ32 and CTLA-4 +49 A/G were detected between male responders and non-responders. A significantly higher prevalence (p = 0.039) of the CTLA-4 +49 AA genotype was found in female responders (42.1%) compared to non-responders (28.9%). Using multiple forward regression analysis, the CTLA-4 +49 AA genotype significantly predicted a positive response to IFN-β therapy in females (p = 0.011) and contributed to 4.5% of response variability. Furthermore, the combined presence of the CCR5Δ32 wtwt/CTLA-4 +49 AA genotype significantly predicted a positive response to treatment in females (p = 0.025). The age at disease onset, pretreatment relapse rate, and baseline EDSS score were not reliable predictors of treatment response in MS patients. Our results indicate that the presence of the CCR5Δ32 polymorphism was not associated with the response to IFN-β treatment, whereas the CTLA-4 +49 polymorphism showed a positive correlation with an optimal response in female patients. [ABSTRACT FROM AUTHOR]

Background and Objective: Patients with spinal muscular atrophy (SMA) treated with a disease-modifying therapy (DMT) are often classified as responders or non-responders based on the attainment of a specific improvement threshold on validated functional scales. This categorization may significantly impact treatment reimbursement in some countries. The aim of this research is to evaluate the perception of treatments and their benefit by patients considered as responders or non-responders. Methods: In this non-commercial multicenter study, 99 post-symptomatically treated SMA type I–III patients with a median age of 11.2 (0.39–57.4) years at treatment initiation were stratified into three groups based on their treatment outcomes, i.e., those exhibiting clinically significant improvement (N = 41), those with non-clinically significant improvement (N = 18), or those showing no improvement (N = 40). Fifteen months after treatment, the initiation patients or patients' caregivers were assessed using a patient-rated scoring system based on the Patient Global Impression of Change (PGIC) scale, comprising 22 questions targeting important aspects and tasks in the daily life of patients with SMA. Results: We found no statistical difference in the patient perception of treatment benefits in 17 out of 22 domains across patient groups. Conclusions: Our results suggest that functional motor scales do not recapitulate patients' and patients' caregivers' experience of the effect of nusinersen treatment in SMA. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were prospectively enrolled in this study as test data. A total of 100 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were retrospectively acquired for training data. Radiomic features were extracted from contrast-enhanced, liver computed tomography (CT) scans obtained before and after TACE. An AI algorithm was trained using the retrospective data and validated using the prospective test data to assess treatment outcomes. Results: This study evaluated 107 radiomic features and 5 clinical characteristics as potential predictors of progression-free survival and overall survival. The C-index was 0.582 as the graph of the cumulative hazard function, predicted by the variable configuration by using 112 radiomics features. The time-dependent AUROC was 0.6 ± 0.06 (mean ± SD). Among the selected radiomics features and clinical characteristics, baseline_glszm_SizeZoneNonUniformity, baseline_ glszm_ZoneVariance and tumor size had excellent performance as predictors of HCC response to TACE with AUROC of 0.853, 0.814 and 0.827, respectively. Conclusions: A radiomics-based AI model is capable of evaluating treatment outcomes for HCC treated with TACE. [ABSTRACT FROM AUTHOR]

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand–receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor–immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements. [ABSTRACT FROM AUTHOR]

Background: Information on the management of non-tuberculous mycobacterial (NTM) lung infection and disease is scarce. The aim of this study was to investigate the trends in NTM lung infections, and the factors associated with the initiation of treatment and treatment outcomes. Methods: A retrospective analysis was carried out on patient medical records from Haukeland University Hospital, Bergen, Norway, from 2000 to 2021. Results: Among 154 patients with NTM lung infection, the majority (70%) were older than 65 years, and 49% had an underlying pulmonary comorbidity. The most frequently observed mycobacterial species was M. avium complex (MAC), followed by M. malmoense and M. abscessus. In total, 72 (47%) patients received antibiotic treatment. Patients with high symptom scores, aged below 65, and with MAC infection had more than three times the odds of receiving antibiotic treatment. A favourable response and culture conversion was observed in 53 of 72 (74%) patients. However, 17 (32%) of them had a relapse. Out of 82 patients who did not receive treatment, 45 (55%) had spontaneous culture conversion, and 8 (18%) of them had a relapse. No factor was identified to be significantly associated with a favourable treatment response. Conclusion: A favourable response to treatment was seen in 74% of patients with a high relapse rate. [ABSTRACT FROM AUTHOR]

This study aims at identifying molecular biomarkers differentiating responders and non-responders to treatment with Tumor Necrosis Factor inhibitors (TNFi) among patients with axial spondyloarthritis (axSpA). Whole blood mRNA and plasma proteins were measured in a cohort of biologic-naïve axSpA patients (n = 35), pre and post (14 weeks) TNFi treatment with adalimumab. Differential expression analysis was used to identify the most enriched pathways and in predictive models to distinguish responses to TNFi. A treatment-associated signature suggests a reduction in inflammatory activity. We found transcripts and proteins robustly differentially expressed between baseline and week 14 in responders. C-reactive protein (CRP) and Haptoglobin (HP) proteins showed strong and early decrease in the plasma of axSpA patients, while a cluster of apolipoproteins (APOD, APOA2, APOA1) showed increased expression at week 14. Responders to TNFi treatment present higher levels of markers of innate immunity at baseline, and lower levels of adaptive immunity markers, particularly B-cells. A logistic regression model incorporating ASDAS-CRP, gender, and AFF3, the top differentially expressed gene at baseline, enabled an accurate prediction of response to adalimumab in our cohort (AUC = 0.97). In conclusion, innate and adaptive immune cell type composition at baseline may be a major contributor to response to adalimumab in axSpA patients. A model including clinical and gene expression variables should also be considered. [ABSTRACT FROM AUTHOR]

Simple Summary: Quantitative assessment of the therapy response in oncological patients undergoing chemo- or immunotherapy is becoming increasingly important not only in the context of clinical studies but also in clinical routine. To facilitate the sometimes complex and time-consuming oncological response assessment, dedicated software solutions, e.g., according to (i)RECIST, have been developed. Considering the higher degree of complexity of iRECIST, we investigated the benefits of software-assisted assessments compared to manual approaches with respect to reader agreement, error rate, and reading time. iRECIST assessments were more feasible and reliable when supported by dedicated software. We conclude that oncologic response assessment in clinical trials should be performed software-assisted rather than manually. Objectives: To compare the feasibility and reliability of manual versus software-assisted assessments of computed tomography scans according to iRECIST in patients undergoing immune-based cancer treatment. Methods: Computed tomography scans of 30 tumor patients undergoing cancer treatment were evaluated by four independent radiologists at baseline (BL) and two follow-ups (FU), resulting in a total of 360 tumor assessments (120 each at BL/FU1/FU2). After image interpretation, tumor burden and response status were either calculated manually or semi-automatically as defined by software, respectively. The reading time, calculated sum of longest diameter (SLD), and tumor response (e.g., "iStable Disease") were determined for each assessment. After complete data collection, a consensus reading among the four readers was performed to establish a reference standard for the correct response assignments. The reading times, error rates, and inter-reader agreement on SLDs were statistically compared between the manual versus software-assisted approaches. Results: The reading time was significantly longer for the manual versus software-assisted assessments at both follow-ups (median [interquartile range] FU1: 4.00 min [2.17 min] vs. 2.50 min [1.00 min]; FU2: 3.75 min [1.88 min] vs. 2.00 min [1.50 min]; both p < 0.001). Regarding reliability, 2.5% of all the response assessments were incorrect at FU1 (3.3% manual; 0% software-assisted), which increased to 5.8% at FU2 (10% manual; 1.7% software-assisted), demonstrating higher error rates for manual readings. Quantitative SLD inter-reader agreement was inferior for the manual compared to the software-assisted assessments at both FUs (FU1: ICC = 0.91 vs. 0.93; FU2: ICC = 0.75 vs. 0.86). Conclusions: Software-assisted assessments may facilitate the iRECIST response evaluation of cancer patients in clinical routine by decreasing the reading time and reducing response misclassifications. [ABSTRACT FROM AUTHOR]

Squamous cell carcinoma of the head and neck (HNSCC) is a globally prevalent form of cancer with significant morbidity and mortality rates. The present study examines the relationship of serum pro-inflammatory cytokines and leptin levels with the effectiveness of therapy in individuals with HNSCC and their potential role as biomarkers for treatment response and toxicity. Induction chemotherapy and concomitant chemoradiotherapy were evaluated for efficacy and safety in 52 individuals with HNSCC. Both response and toxicity were evaluated, and serum levels of pro-inflammatory cytokines Interlukin-1 beta (IL-1β), Interlukin-2 (IL-2), Interlukin-6 (IL-6), and Tumor Necrosis Factor-Alpha (TNF-α) and leptin were measured using enzyme-linked immunoassay before and after treatment. Before treatment, these measurements were made in comparison with a control group with 50 healthy people. The results showed that serum cytokines and leptin levels varied depending on the response to treatment, with patients who had a complete or partial response (PR) showing significant decreases in IL-1 β, IL-6, and TNF-α levels and significant increases in IL-2 and leptin levels after treatment, with an improvement in cachexia. These results imply that variations in serum pro-inflammatory cytokines and leptin levels are likely related to the therapeutic effectiveness in HNSCC and may act as biomarkers for treatment response. [ABSTRACT FROM AUTHOR]

Fingolimod is an immunomodulatory sphingosine-1-phosphate (S1P) analogue approved for the treatment of relapsing-remitting multiple sclerosis (RRMS). The identification of biomarkers of clinical responses to fingolimod is a major necessity in MS to identify optimal responders and avoid the risk of disease progression in non-responders. With this aim, we used RNA sequencing to study the transcriptomic changes induced by fingolimod in peripheral blood mononuclear cells of MS-treated patients and their association with clinical response. Samples were obtained from 10 RRMS patients (five responders and five non-responders) at baseline and at 12 months of fingolimod therapy. Fingolimod exerted a vast impact at the transcriptional level, identifying 7155 differentially expressed genes (DEGs) compared to baseline that affected the regulation of numerous signaling pathways. These DEGs were predominantly immune related, including genes associated with S1P metabolism, cytokines, lymphocyte trafficking, master transcription factors of lymphocyte functions and the NF-kB pathway. Responder and non-responder patients exhibited a differential transcriptomic regulation during treatment, with responders presenting a higher number of DEGs (6405) compared to non-responders (2653). The S1P, NF-kB and TCR signaling pathways were differentially modulated in responder and non-responder patients. These transcriptomic differences offer the potential of being exploited as biomarkers of a clinical response to fingolimod. [ABSTRACT FROM AUTHOR]

The vitamin D receptor (VDR), binding to the active form of the vitamin, promotes the transcription of numerous genes involved in the proliferation of immune cells, cytokine production and lymphocyte activation. It is known that vitamin D deficiency can influence the risk of developing rheumatoid arthritis (RA) or modulate its disease activity. The aim of this study was to investigate a possible association between the rs11568820 (C > T) polymorphism in the promoter region of VDR gene and the response to therapy with anti-TNF drugs in patients with RA. A total of 178 consecutive Italian patients with RA treated with anti-TNF, naïve for biological therapy, were recruited. Disease activity data were evaluated using specific indices such as DAS28, CDAI and SDAI, measured at the start of therapy and subsequently at 22, 52, 104 and 240 weeks. A statistically significant association emerged between the rs11568820 variant allele of VDR gene and failure to remission assessed by CDAI and SDAI at 52 weeks, and by DAS28, CDAI and SDAI at 104 weeks of follow-up. Furthermore, the variant allele of this polymorphism was observed more frequently in patients who did not undergo sustained remission calculated by CDAI and SDAI. The variant T allele of rs11568820 in VDR gene is associated with a reduced remission rate with anti-TNFα drugs. These data suggest the role of VDR genetic variability in the response to therapy and in the achievement of remission. [ABSTRACT FROM AUTHOR]

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Tenosynovial giant cell tumor is a benign yet aggressive neoplasm of the synovium that predominantly affects young patients. The tumor comprises two subtypes: the localized type and diffuse type, with the diffuse type exhibiting significantly higher aggressiveness. MRI stands out as the most valuable imaging modality for both its diagnosis and planning its treatment. When interpreting the initial MRI for suspected tenosynovial giant cell tumor, it is imperative to consider: (i) the characteristic findings of tenosynovial giant cell tumor, (ii) the potential findings of the diffuse type, and (iii) the tumor's resectability. In interpreting follow-up MRIs of the diffuse type after treatment, it is crucial to consider both local recurrence and the development of early osteoarthritis after surgery as well as the treatment response after systemic treatment. Recognizing the distinctive MRI findings of diffuse type tenosynovial giant cell tumor before and after treatment enhances radiologic evaluation, contributing to optimal patient management. Tenosynovial giant cell tumor (TSGCT) is a rare soft tissue tumor that involves the synovial lining of joints, bursae, and tendon sheaths, primarily affecting young patients (usually in the fourth decade of life). The tumor comprises two subtypes: the localized type (L-TSGCT) and the diffuse type (D-TSGCT). Although these subtypes share histological and genetic similarities, they present a different prognosis. D-TSGCT tends to exhibit local aggressiveness and a higher recurrence rate compared to L-TSGCT. Magnetic resonance imaging (MRI) is the preferred diagnostic tool for both the initial diagnosis and for treatment planning. When interpreting the initial MRI of a suspected TSGCT, it is essential to consider: (i) the characteristic findings of TSGCT—evident as low to intermediate signal intensity on both T1- and T2-weighted images, with a blooming artifact on gradient-echo sequences due to hemosiderin deposition; (ii) the possibility of D-TSGCT—extensive involvement of the synovial membrane with infiltrative margin; and (iii) the resectability and extent—if resectable, synovectomy is performed; if not, a novel systemic therapy involving colony-stimulating factor 1 receptor inhibitors is administered. In the interpretation of follow-up MRIs of D-TSGCTs after treatment, it is crucial to consider both tumor recurrence and potential complications such as osteoarthritis after surgery as well as the treatment response after systemic treatment. Given its prevalence in young adult patents and significant impact on patients' quality of life, clinical trials exploring new agents targeting D-TSGCT are currently underway. Consequently, understanding the characteristic MRI findings of D-TSGCT before and after treatment is imperative. [ABSTRACT FROM AUTHOR]

Simple Summary: Although shear-wave elastography has been utilized in diagnosing a malignant breast lesion and axillary lymph node metastasis, its potential role in predicting treatment response to neoadjuvant chemotherapy has not been thoroughly explored. In this study, we aimed to assess the possibility of elasticity values measured using SWE as a predictive marker for neoadjuvant chemotherapy in breast cancer. Our findings indicate that low tumor stiffness, as measured using SWE, was significantly associated with an excellent treatment response following neoadjuvant chemotherapy. This relationship was particularly evident in hormone-receptor-positive, HER2-negative breast cancer, and triple-negative breast cancer. Furthermore, we identified an inverse correlation between tumor stiffness and the tumor-infiltrating lymphocyte level, suggesting that tumors with high TIL levels tend to exhibit lower stiffness. Our findings suggest that SWE could be a useful tool in predicting treatment response and guiding treatment decisions in neoadjuvant chemotherapy for patients with breast cancer. Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC. [ABSTRACT FROM AUTHOR]

Rectal cancer presents significant diagnostic and therapeutic challenges, with neoadjuvant therapy playing a pivotal role in improving resectability and patient outcomes. MRI serves as a critical tool in assessing treatment response. However, differentiating viable tumor tissue from therapy-induced changes on MRI remains a complex task. In this comprehensive review, we explore treatment options for rectal cancer based on resectability status, focusing on the role of MRI in guiding therapeutic decisions. We delve into the nuances of MRI-based evaluation of treatment response following neoadjuvant therapy, paying particular attention to emerging techniques like radiomics. Drawing from our insights based on the literature, we provide essential recommendations for post-neoadjuvant therapy management of rectal cancer, all within the context of MRI-based findings. [ABSTRACT FROM AUTHOR]

Simple Summary: Amino acids are the fundamental building blocks of proteins and play a crucial role in various cellular functions, such as protein metabolism/catabolism pathways and redox signaling. They are also vital in normal and cancer metabolism. The concentrations of amino acids in blood, urine, and tissue samples of cancer patients differ from those of healthy individuals. These differences can be used for cancer screening, monitoring treatment response, and predicting tumor prognosis. Colorectal cancer (CRC) is one of the most common and deadly cancers. This review aims to gather reported amino acid abnormalities in blood, urine, and tissue samples of CRC patients and how these abnormalities can be used as potential tools in the multifold management of CRC, including screening, establishing cancer prognosis, and monitoring treatment response. Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients. [ABSTRACT FROM AUTHOR]

The adoption of neoadjuvant concurrent chemoradiotherapy (CCRT) has reshaped the therapeutic landscape, but response prediction remains challenging. This study investigates the interaction between pre-CCRT carcinoembryonic antigen (CEA) and post-CCRT hemoglobin (Hb) levels in predicting the response of locally advanced rectal cancer (LARC) to CCRT. Retrospective data from 93 rectal cancer patients receiving neoadjuvant CCRT were analyzed. Univariate analyses assessed clinical factors associated with tumor regression grade (TRG) and T-stage outcomes. Machine learning identified predictive biomarkers. Interaction effects between CEA and Hb were explored through subgroup analyses. Post-CCRT Hb varied between pre-CCRT CEA groups. The interaction between pre-CCRT CEA and post-CCRT Hb influenced TRG. Males with normal pre-CCRT CEA and anemia showed better treatment responses. Females with elevated pre-CCRT CEA and post-CCRT anemia exhibited poorer responses. The interaction effect between them was significant, indicating that their relationship with TRG was not additive. Inflammatory biomarkers, WBC, neutrophil count, and post-CCRT platelet level correlated with CCRT response. Contrasting with previous findings, anemia was a predictor of better treatment response in males with normal pre-CCRT CEA. The interaction between pre-CCRT CEA and post-CCRT Hb levels predicts the response of LARC to CCRT. CEA, Hb, and sex should be considered when assessing treatment response. Inflammatory biomarkers contribute to response prediction. Understanding these complex relationships can enhance personalized treatment approaches in rectal cancer patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Rectal tumour biological characteristics play an important role in determining treatment regimen, predicting treatment response and predicting prognosis. Currently, obtaining tumour biological information requires costly, invasive and time-consuming genetic testing. Radiogenomics, referring to the extraction of imaging biomarkers that may serve as identifiers for specific biological characteristics, serves as a non-invasive alternative to genetic testing. Our study aims to collate the current evidence for radiogenomics in the field of rectal cancer, highlighting strengths and weaknesses of individual studies. Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC. [ABSTRACT FROM AUTHOR]

Simple Summary: The expression of the cysteine–glutamate cotransporter, system xc−, is increased in cancer cells across many cancer types. Imaging system xc− provides new insights into tumour behaviour. The radiotracer (4S)-4-(3-[18F]Fluoropropyl)-L-glutamic acid (18F-FSPG) is specifically transported by system xc−, allowing for a non-invasive method of measuring this transporter's activity. This review summarises the data available on the use of 18F-FSPG in human cancer patients, exploring its advantages and disadvantages, and suggests possible future uses of 18F-FSPG in the assessment of early treatment response and treatment resistance. System xc− is upregulated in cancer cells and can be imaged using novel radiotracers, most commonly with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid (18F-FSPG). The aim of this review was to summarise the use of 18F-FSPG in humans, explore the benefits and limitations of 18F-FSPG, and assess the potential for further use of 18F-FSPG in cancer patients. To date, ten papers have described the use of 18F-FSPG in human cancers. These studies involved small numbers of patients (range 1–26) and assessed the use of 18F-FSPG as a general oncological diagnostic agent across different cancer types. These clinical trials were contrasting in their findings, limiting the scope of 18F-FSPG PET/CT as a purely diagnostic agent, primarily due to heterogeneity of 18F-FSPG retention both between cancer types and patients. Despite these limitations, a potential further application for 18F-FSPG is in the assessment of early treatment response and prediction of treatment resistance. Animal models of cancer have shown that changes in 18F-FSPG retention following effective therapy precede glycolytic changes, as indicated by 18F-FDG, and changes in tumour volume, as measured by CT. If these results could be replicated in human clinical trials, imaging with 18F-FSPG PET/CT would offer an exciting route towards addressing the currently unmet clinical needs of treatment resistance prediction and early imaging assessment of therapy response. [ABSTRACT FROM AUTHOR]

Simple Summary: Oxidative stress has been implicated in the development, proliferation, and metastasis of colorectal cancer. In this study, we investigated whether the rate of change in reactive oxygen metabolite derivatives (d-ROM)—serum markers of oxidative stress—could predict treatment response in metastatic colorectal cancer. We enrolled 53 patients with metastatic colorectal cancer who were treated with 3 months of chemotherapy. We measured d-ROM levels before and after chemotherapy and examined the change in d-ROM levels for each anticancer treatment. Factors influencing the d-ROM ratio (post-treatment: pre-treatment levels) were examined using linear regression analysis. d-ROM levels decreased in patients showing a partial response and increased in those showing disease progression. An increasing d-ROM ratio was associated with disease progression. Our study indicates that d-ROM levels are useful markers of tumor progression and that the d-ROM ratio is useful for predicting treatment response in patients with metastatic colorectal cancer. Oxidative stress has been implicated in the development, proliferation, and metastasis of colorectal cancer, but few studies have considered how oxidative stress changes in relation to treatment response. In this study, we investigated whether the rate of change in reactive oxygen metabolite derivatives (d-ROM)—serum markers of oxidative stress—could predict treatment response in metastatic colorectal cancer. We enrolled 53 patients with metastatic colorectal cancer who were treated with 3 months of chemotherapy. We measured d-ROM levels and performed computed tomography before and after chemotherapy, and we examined the change in d-ROM levels for each anticancer treatment. Factors influencing the d-ROM ratio (post-treatment: pre-treatment levels) were examined using linear regression analysis. d-ROM levels decreased in patients showing a partial response (p < 0.001) and increased in those showing disease progression (p = 0.042). An increasing d-ROM ratio was associated with disease progression (regression coefficient: 0.416, 95% confidence interval: 0.279–0.555, p < 0.001). Our study indicates that d-ROM levels are useful markers of tumor progression and that the d-ROM ratio is useful for predicting treatment response in patients with metastatic colorectal cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: In hepatocellular carcinoma (HCC), the clinical predictive factors for tumor markers are well-known. Although these factors are recognized as essential, recent attempts have been made to predict treatment outcomes using radiomics based on imaging markers. We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in 409 patients with HCC who received liver-directed combined radiotherapy (LD-CRT). In predicting the OR and IFFR, clinical models and radiomics models based on tumoral and peritumoral areas showed an acceptable performance, while combined clinico-radiomics models (CCR) performed better. Therefore, CCR models have potential use in clinical prediction. Moreover, the constructed nomograms based on these models may provide valuable information on the OR and IFFR in patients with HCC undergoing LD-CRT. Purpose: We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT). Methods: We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model). Results: Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models. Conclusion: In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients. [ABSTRACT FROM AUTHOR]

Pancreatic adenocarcinoma (PDAC) is the most common pancreatic cancer and is associated with poor prognosis, a high mortality rate, and a substantial number of healthy life years lost. Surgical resection is the primary treatment option for patients with resectable disease; however, only 10–20% of all patients with PDAC are eligible for resection at the time of diagnosis. In this context, neoadjuvant therapy has the potential to increase the number of patients who are eligible for resection, thereby improving the overall survival rate. For patients who undergo neoadjuvant therapy, computed tomography (CT) remains the primary imaging tool for assessing treatment response. Nevertheless, the interpretation of imaging findings in this context remains challenging, given the similarity between viable tumor and treatment-related changes following neoadjuvant therapy. In this review, following an overview of the various treatment options for PDAC according to its resectability status, we will describe the key challenges regarding CT-based evaluation of PDAC treatment response following neoadjuvant therapy, as well as summarize the literature on CT-based evaluation of PDAC treatment response, including the use of radiomics. Finally, we will outline key recommendations for the management of PDAC after neoadjuvant therapy, taking into consideration CT-based findings. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is considered as the most common malignancy among females, and its treatment takes many forms and types. Neoadjuvant chemotherapy (NACT), which is the treatment precedes the surgical intervention, became the preferred treatment approach for some subtypes of breast tumors. However, some patients exhibit good response to the neoadjuvant treatment, while others do not. Therefore, the proactive prediction of patients' response to NACT is a necessity to reduce the exposure to unnecessary doses of treatment, treatment costs, and side effects. Many researchers proposed prediction models to predict patients' response to NACT either at early stage of treatment or prior to the initiation of the first cycle. They used various radiomics, pathological, and clinical predictors and markers. This review discusses some of the researches conducted the last decade based on statistical, machine learning, or deep learning approaches. Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: In this comprehensive review, we aimed to summarize the advances made by artificial intelligence in the field of lung cancer screening, diagnosis, and management. We now understand the utility of AI as a tool that can supplement physicians to improve the quality of care provided, which is the core message of this review, along with the relevant literature supporting the advances. Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the main causes of death worldwide. Lately, there is great interest in developing methods that assess individual sensitivity and/or resistance of tumors to antineoplastics to provide personalized therapy for patients. In this study we used organotypic culture of human breast tumor slices to predict the experimental effect of antineoplastics on the viability of tumoral tissue. Samples of breast tumor were taken from 27 patients with clinically advanced breast cancer; slices were obtained and incubated separately for 48 h with paclitaxel, docetaxel, epirubicin, 5-fluorouracil, cyclophosphamide, and cell culture media (control). We determined an experimental tumor sensitivity/resistance (S/R) profile by evaluating tissue viability using the Alamar Blue® metabolic test, and by structural viability (histopathological analyses, necrosis, and inflammation). These parameters were related to immunohistochemical expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. The predominant histological type found was infiltrating ductal carcinoma (85.2%), followed by lobular carcinoma (7.4%) and mixed carcinoma (7.4%). Experimental drug resistance was related to positive hormone receptor status in 83% of samples treated with cyclophosphamide (p = 0.027). Results suggest that the tumor S/R profile can help to predict personalized therapy or optimize chemotherapeutic treatments in breast cancer. [ABSTRACT FROM AUTHOR]

Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression. [ABSTRACT FROM AUTHOR]

Therapeutic options for inflammatory bowel diseases (IBD) have largely expanded in the last decades, both in Crohn's disease and ulcerative colitis, including multiple biological drugs targeting different inflammation pathways. However, choosing the best treatment and timing for each patient is still an undeniable challenge for IBD physicians due to the marked heterogeneity among patients and disease behavior. Therefore, early prediction of the response to biological drugs becomes of utmost importance, allowing prompt optimization of therapeutic strategies and thus paving the way towards precision medicine. In such a context, researchers have recently focused on cross-sectional imaging techniques (intestinal ultrasound, computed tomography, and magnetic resonance enterography) in order to identify predictive markers of response or non-response to biologic therapies. In this review, we aim to summarize data about imaging factors that may early predict disease behavior during biological treatment, potentially helping to define more precise and patient-tailored strategies. [ABSTRACT FROM AUTHOR]

Brain-derived neurotrophic factor (BDNF) is implicated in the etiology and treatment response in major depressive disorder (MDD). However, peripheral BDNF concentrations have not been compared across different MDD stages. Bright light therapy (BLT) offers some potential in treatment-resistant depression (TRD), but its effects on BDNF levels are unknown. This study included a cross-sectional analysis of plasma BDNF concentration in females with TRD, unmedicated MDD patients, and healthy controls (HC), and measurements of longitudinal BLT effects on plasma BDNF levels in TRD patients. The present study included 55 drug-naïve, first-episode patients, 25 drug-free recurrent-episode MDD patients, 71 HC participants, and 54 TRD patients. Patients were rated by Hamilton Depression Rating Scale (HAMD)-17 and the Montgomery–Åsberg Depression Rating Scale (MADRS). Patients with TRD received BLT during 4 weeks. The total HAMD-17 and MADRS scores decreased following BLT. All patient groups had lower plasma BDNF than HC, but BDNF levels did not differ between first- and recurrent-episode BDNF patients and TRD patients before or after BLT. However, responders and remitters to BLT had higher post-treatment plasma BDNF concentrations than patients who did not achieve response or remission. The changes in plasma BDNF levels may be candidates for biomarkers of treatment response to BLT in TRD patients. [ABSTRACT FROM AUTHOR]

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response. [ABSTRACT FROM AUTHOR]

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used psychopharmaceutical treatment for major depressive disorder (MDD), but individual responses to SSRIs vary greatly. CYP2C19 is a key enzyme involved in the metabolism of several drugs, including SSRIs. Variations in the CYP2C19 gene are associated with differential metabolic activity, and thus differential SSRI exposure; accordingly, the CYP2C19 genotype may affect the therapeutic response and clinical outcomes, though existing evidence of this link is not entirely consistent. Therefore, we analysed data from the UK Biobank, a large, deeply phenotyped prospective study, to investigate the effects of CYP2C19 metaboliser phenotypes on several clinical outcomes derived from primary care records, including multiple measures of antidepressant switching, discontinuation, duration, and side effects. In this dataset, 24,729 individuals were prescribed citalopram, 3012 individuals were prescribed escitalopram, and 12,544 individuals were prescribed sertraline. Consistent with pharmacological expectations, CYP2C19 poor metabolisers on escitalopram were more likely to switch antidepressants, have side effects following first prescription, and be on escitalopram for a shorter duration compared to normal metabolisers. CYP2C19 poor and intermediate metabolisers on citalopram also exhibited increased odds of discontinuation and shorter durations relative to normal metabolisers. Generally, no associations were found between metabolic phenotypes and proxies of response to sertraline. Sensitivity analyses in a depression subgroup and metabolic activity scores corroborated results from the primary analysis. In summary, our findings suggest that CYP2C19 genotypes, and thus metabolic phenotypes, may have utility in determining clinical responses to SSRIs, particularly escitalopram and citalopram, though further investigation of such a relationship is warranted. [ABSTRACT FROM AUTHOR]

Background: The study investigated whether three deep-learning models, namely, the CNN_model (trained from scratch), the TL_model (transfer learning), and the FT_model (fine-tuning), could predict the early response of brain metastases (BM) to radiosurgery using a minimal pre-processing of the MRI images. The dataset consisted of 19 BM patients who underwent stereotactic-radiosurgery (SRS) within 3 months. The images used included axial fluid-attenuated inversion recovery (FLAIR) sequences and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences from the tumor center. The patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). Methods: A total of 2320 images from the regression class and 874 from the progression class were randomly assigned to training, testing, and validation groups. The DL models were trained using the training-group images and labels, and the validation dataset was used to select the best model for classifying the evaluation images as showing regression or progression. Results: Among the 19 patients, 15 were classified as "responders" and 4 as "non-responders". The CNN_model achieved good performance for both classes, showing high precision, recall, and F1-scores. The overall accuracy was 0.98, with an AUC of 0.989. The TL_model performed well in identifying the "progression" class, but could benefit from improved precision, while the "regression" class exhibited high precision, but lower recall. The overall accuracy of the TL_model was 0.92, and the AUC was 0.936. The FT_model showed high recall for "progression", but low precision, and for the "regression" class, it exhibited a high precision, but lower recall. The overall accuracy for the FT_model was 0.83, with an AUC of 0.885. Conclusions: Among the three models analyzed, the CNN_model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images. This suggests that CNN models could potentially predict SRS prognoses from small datasets. However, further analysis is needed, especially in cases where class imbalances exist. [ABSTRACT FROM AUTHOR]

Simple Summary: [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) provides information about metabolic patterns of different diseases and conditions. This study aimed to prospectively evaluate patients with breast cancer in order to describe specific brain metabolic patterns related to the presence or absence of primary forms of headache, namely tension-type headache (TTH) and migraine (MiG). Moreover, we explored the association between primary headache forms and BC response to neoadjuvant chemotherapy (NAC). We observed a high rate of headache in the 46 BC analyzed patients. TTH patients exhibited areas of hypometabolism in specific brain regions before NAC. Moreover, our results suggest an association between primary headache, especially MiG, and treatment response to NAC. Collectively, our results support the hypothesis of a complex and dynamic interplay among BC, headache, and hormonal status. This study aimed to examine brain metabolic patterns on [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) in breast cancer (BC), comparing patients with tension-type headache (TTH), migraine (MiG), and those without headache. Further association with BC response to neoadjuvant chemotherapy (NAC) was explored. In this prospective study, BC patients eligible for NAC performed total-body [18F]FDG PET/CT with a dedicated brain scan. A voxel-wise analysis (two-sample t-test) and a multiple regression model were used to compare brain metabolic patterns among TTH, MiG, and no-headache patients and to correlate them with clinical covariates. A single-subject analysis compared each patient's brain uptake before and after NAC with a healthy control group. Primary headache was diagnosed in 39/46 of BC patients (39% TTH and 46% MiG). TTH patients exhibited hypometabolism in specific brain regions before NAC. TTH patients with a pathological complete response (pCR) to NAC showed hypermetabolic brain regions in the anterior medial frontal cortex. The correlation between tumor uptake and brain metabolism varied before and after NAC, suggesting an inverse relationship. Additionally, the single-subject analysis revealed that hypometabolic brain regions were not present after NAC. Primary headache, especially MiG, was associated with a better response to NAC. These findings suggest complex interactions between BC, headache, and hormonal status, warranting further investigation in larger prospective cohorts. [ABSTRACT FROM AUTHOR]

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS–SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS–SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward–behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants. [ABSTRACT FROM AUTHOR]

Simple Summary: Tyrosine kinase inhibitors (TKIs) have significantly improved treatment outcomes in pediatric patients with chronic myeloid leukemia (CML). However, there is insufficient evidence suggesting the superiority of one TKI over another in terms of treatment response and long-term adverse events (AEs). We aimed to assess the efficacy and safety profiles of front-line TKIs among pediatric patients. The complete cytogenetic response rates were excellent for both imatinib and dasatinib at 12 months. However, patients treated with dasatinib exhibited significantly faster and higher cumulative rates of early, major, and deep molecular responses. Although both TKIs were well tolerated, a notable decline in height was observed with both TKIs, with a greater decrease observed in the dasatinib group during the last year of observation. Our findings confirm the efficacy of both TKIs; however, the long-term AEs associated with their use should be evaluated in a large cohort of pediatric patients. We conducted a retrospective study on 51 pediatric patients with newly diagnosed chronic myeloid leukemia chronic phase or accelerated phase. The patients were classified into the IMA group (N = 33), treated with imatinib, and the DSA group (N = 18), treated with dasatinib, as front-line tyrosine kinase inhibitors (TKIs). At 12 months, the rates of complete cytogenetic response were similar between the IMA group (92.3%) and DSA group (100%) (p = 0.305). However, the rate of early molecular response was higher in the DSA group than in the IMA group (100.0% vs. 80.0%, p = 0.043). By 12 and 24 months, the DSA group showed faster and higher cumulative rates of both major (DSA group: 72.2% and 100%, respectively; IMA group: 41.2% and 68.7%, respectively; p = 0.002) and deep molecular responses (DSA group: 26.0% and 43.6%, respectively; IMA group: 13.8% and 17.5%, respectively; p = 0.004). Both TKIs were well tolerated. Although the height standard deviation scores decreased in both groups, the height decline was greater in the DSA group between one and two years from the start of TKI therapy. In this study, dasatinib achieved faster and higher molecular responses with an acceptable safety profile. Further follow-up is necessary to assess the long-term outcomes of TKI treatment in children. [ABSTRACT FROM AUTHOR]

Retinoid X receptor (RXR) heterodimerizes with the PPAR nuclear hormone receptor and regulates its downstream events. We investigated the effects of RXR agonists (LG100754, bexarotene, AGN194204, and LG101506) on lenalidomide's anti-myeloma activity, T cell functions, and the level of glucose and lipids in vivo. Genetic overexpression and CRISPR/Cas9 knockout experiments were conducted in multiple myeloma (MM) cell lines and Jurkat T cell lines to determine the roles of CRBN in RXR-agonist mediated effects. A xenograft mouse model of MM was established to determine the combination effect of LG100754 and lenalidomide. The combination of RXR agonists and lenalidomide demonstrated synergistic activity in increasing CRBN expression and killing myeloma cells. Mechanistically, the RXR agonists reduced the binding of PPARs to the CRBN promoter, thereby relieving the repressor effect of PPARs on CRBN transcription. RXR agonists downregulated the exhaustion markers and increased the activation markers of Jurkat T cells and primary human T cells. Co-administration of LG100754 and lenalidomide showed enhanced anti-tumor activity in vivo. LG100754 retained its glucose- and lipid-lowering effects. RXR agonists demonstrate potential utility in enhancing drug sensitivity and T-cell function in the treatment of myeloma. [ABSTRACT FROM AUTHOR]

Immunoglobulin (Ig) G4 accounts for 4–6% of the total IgG in a healthy human. Several evidence-based studies have suggested that the level of IgG4 is significantly elevated in autoimmune diseases, including rheumatoid arthritis (RA). The clinical significance of IgG4 in RA with regard to disease activity, severity, and treatment response remains elusive. We consecutively recruited 174 patients with RA from our rheumatology clinic. All subjects were assessed for their disease activity based on DAS28, radiographic joint damage based on the Modified Sharp Score (MSS), the functional capacity based on the Health Assessment Questionnaire –Disability Index (HAQ-DI), and treatment responsiveness using the European League Against Rheumatism (EULAR) response criteria. The serum IgG4 of the recruited subjects was measured via the ELISA test. The mean serum IgG4 level was 60.23 ± 30.08 mg/dL. We found that serum IgG4 had significant positive correlations with disease activity (r = 0.406; p < 0.001), ESR (r = 0.155; p = 0.041), CRP (r = 0.269; p < 0.001), joint damage (r = 0.195; p = 0.012) and functional disability (r = 0.909; p < 0.001). Subjects with elevated IgG4 (IgG4 > 86 mg/dL) had significantly higher ESR, CRP, HAQ-DI, and DAS 28 and a poorer treatment response compared to the group with non-elevated IgG4. After multivariate analysis, only HAQ-DI (OR = 4.229, 95% CI 1.302, 15.751, p = 0.018) and DAS28 (OR = 3.743, 95% CI 1.062, 13.193, p = 0.040) remained significantly associated with elevated serum IgG4. The preliminary findings of this study could suggest serum IgG4 to be a potential biomarker of disease activity and functional disability in RA. [ABSTRACT FROM AUTHOR]

Chronic urticaria (CU) is a debilitating skin disease affecting around 1% of the population. CU can be subdivided into chronic spontaneous urticaria (CSU) and chronic inducible urticaria (CIndU). Different pathophysiological mechanisms have been proposed to play a role in the development of CU, and these are also being investigated as potential biomarkers in the diagnosis and management of the disease. As of now the only assessment tools available for treatment response are patient reported outcomes (PROs). Although these tools are both validated and widely used, they leave a desire for more objective measurements. A biomarker is a broad subcategory of observations that can be used as an accurate, reproducible, and objective indicator of clinically relevant outcomes. This could be normal biological or pathogenic processes, or a response to an intervention or exposure, e.g., treatment response. Herein we provide an overview of biomarkers for CU, with a focus on prognostic biomarkers for treatment response to omalizumab, thereby potentially aiding physicians in personalizing treatments. [ABSTRACT FROM AUTHOR]

Psychotic disorders are complex disorders with multiple etiologies. While increased dopamine synthesis capacity has been proposed to underlie psychotic episodes, dopamine-independent processes are also involved (less responsive to dopamine receptor-blocking medications). The underlying mechanism(s) of the reduction in antipsychotic responsiveness over time, especially after repeated relapses, remain unclear. Despite the consistent evidence of dopamine overactivity and hippocampal volume loss in schizophrenia, few accounts have been provided based on the interactive effect of dopamine on hippocampal synapse plasticity mediating autobiographical memory processes. The present hypothesis builds upon previous works showing the potential effects of dopamine overactivity on hippocampal-mediated neuroplasticity underlying autobiographical memory, alongside known patterns of autobiographical memory dysfunction in psychosis. We propose that spurious autobiographical memory of psychosis (SAMP) produced during active psychosis may be a key mechanism mediating relapses and treatment non-responsiveness. In a hyperdopaminergic state, SAMP is expected to be generated at an increased rate during active psychosis. Similar to other memories, it will undergo assimilation, accommodation, and extinction processes. However, if SAMP fails to integrate with existing memory, a discontinuity in autobiographical memory may result. Inadequate exposure to normalizing experiences and hyposalience due to overmedication or negative symptoms may also impede the resolution of SAMP. Residual SAMP is hypothesized to increase the propensity for relapse and treatment non-responsiveness. Based on recent findings on the role of dopamine in facilitating hippocampal synapse plasticity and autobiographical memory formation, the SAMP hypothesis is consistent with clinical observations of DUP effects, including the repetition of contents in psychotic relapses as well as the emergence of treatment non-responsiveness after repeated relapses. Clinical implications of the hypothesis highlight the importance of minimizing active psychosis, integrating psychosis memory, avoiding over-medication, and fostering normalizing experiences. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the publication in 2016 of the WHO's classification of primary brain tumors according to their histopathology but also their molecular status (IDH, 1p/19q codeletion), oligodendrogliomas defined by the presence of the 1p/19q codeletion have been clearly identified as having a better prognosis. However, the response to treatment of 1p/19q codeleted gliomas remains heterogeneous. Very few studies have investigated the genetic profiles of these tumors, particularly with regard to their response to treatment (radiotherapy and chemotherapy). Our analyses revealed a gene signature composed of eight genes involved in metabolism, immunity, and extracellular matrix organization pathways that were associated with a poor response to treatment for 1p/19q codeleted tumors. This signature could be used in the future to identify patients who need more intensive treatment, potentially with inhibitors of these pathways. Background. IDH mutant and 1p/19q codeleted oligodendrogliomas are the gliomas associated with the best prognosis. However, despite their sensitivity to treatment, patient survival remains heterogeneous. We aimed to identify gene expressions associated with response to treatment from a national cohort of patients with oligodendrogliomas, all treated with radiotherapy +/− chemotherapy. Methods. We extracted total RNA from frozen tumor samples and investigated enriched pathways using KEGG and Reactome databases. We applied a stability selection approach based on subsampling combined with the lasso-pcvl algorithm to identify genes associated with progression-free survival and calculate a risk score. Results. We included 68 patients with oligodendrogliomas treated with radiotherapy +/− chemotherapy. After filtering, 1697 genes were obtained, including 134 associated with progression-free survival: 35 with a better prognosis and 99 with a poorer one. Eight genes (ST3GAL6, QPCT, NQO1, EPHX1, CST3, S100A8, CHI3L1, and OSBPL3) whose risk score remained statistically significant after adjustment for prognostic factors in multivariate analysis were selected in more than 60% of cases were associated with shorter progression-free survival. Conclusions. We found an eight-gene signature associated with a higher risk of rapid relapse after treatment in patients with oligodendrogliomas. This finding could help clinicians identify patients who need more intensive treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: This study was a retrospective investigation of a Japanese cohort of 205 metastatic castration-resistant prostate cancer (mCRPC) patients who received Ra-223 in 14 hospitals between July 2016 and August 2020 and for whom bone scintigraphy before and after the radium-223 treatment was available. Following treatment, alkaline phosphatase (ALP) decline (%ALP < 0%) was noted in 72.2% (148/205), automated bone scan index (aBSI) decline (%aBSI < 0%) in 52.7% (108/205), and PSA decline (%PSA < 0%) in 27.8% (57/205). Furthermore, a reduction in both aBSI and ALP was seen in 87 (42.4%), a reduction in only ALP was seen in 61 (29.8%), a reduction in only aBSI was seen in 21 (10.2%), and in both aBSI and ALP increasing/stable (≥0%) was seen in 36 (17.6%) patients. Multiparametric analysis showed changes in PSA (HR 4.30, 95% CI 2.32–8.77, p < 0.0001), aBSI (HR 2.22, 95%CI 1.43–3.59, p = 0.0003), and ALP (HR 2.06, 95%CI 1.35–3.14, p = 0.0008) as significant prognostic factors for OS. For mCRPC patients treated with Ra-223, aBSI change is useful as an imaging biomarker for treatment response assessment and survival prediction. To evaluate the usefulness of change in the automated bone scan index (aBSI) value derived from bone scintigraphy findings as an imaging biomarker for the assessment of treatment response and survival prediction in metastatic castration-resistant prostate cancer (mCRPC) patients treated with Ra-223. This study was a retrospective investigation of a Japanese cohort of 205 mCRPC patients who received Ra-223 in 14 hospitals between July 2016 and August 2020 and for whom bone scintigraphy before and after radium-223 treatment was available. Correlations of aBSI change, with changes in the serum markers alkaline phosphatase (ALP) and prostate-specific antigen (PSA) were evaluated. Additionally, the association of those changes with overall survival (OS) was assessed using the Cox proportional-hazards model and Kaplan–Meier curve results. Of the 205 patients enrolled, 165 (80.5%) completed six cycles of Ra-223. Following treatment, ALP decline (%ALP < 0%) was noted in 72.2% (148/205), aBSI decline (%aBSI < 0%) in 52.7% (108/205), and PSA decline (%PSA < 0%) in 27.8% (57/205). Furthermore, a reduction in both aBSI and ALP was seen in 87 (42.4%), a reduction in only ALP was seen in 61 (29.8%), a reduction in only aBSI was seen in 21 (10.2%), and in both aBSI and ALP increasing/stable (≥0%) was seen in 36 (17.6%) patients. Multiparametric analysis showed changes in PSA [hazard ratio (HR) 4.30, 95% confidence interval (CI) 2.32–8.77, p < 0.0001], aBSI (HR 2.22, 95%CI 1.43–3.59, p = 0.0003), and ALP (HR 2.06, 95%CI 1.35–3.14, p = 0.0008) as significant prognostic factors for OS. For mCRPC patients treated with Ra-223, aBSI change is useful as an imaging biomarker for treatment response assessment and survival prediction. [ABSTRACT FROM AUTHOR]

The hepatitis B virus core protein (HBcAg) is a highly immunogenic particulate antigen. Nearly all patients with persistent or resolved hepatitis B virus (HBV) infection show seropositivity for hepatitis B core antibody (anti-HBc), which appears in the early stage of infection and is mostly present for life. Traditionally, the anti-HBc is regarded as an evidential serological marker of HBV infections. In the last ten years, several studies revealed the predictive value of quantitative anti-HBc (qAnti-HBc) level in the treatment response and clinical outcome of chronic HBV infections, implying new insights into this classic marker. Overall, qAnti-HBc should be regarded as an indicator of the host's immune response specific to HBV, which correlates with HBV-related hepatitis activity and liver pathology. This review summarized the latest understanding of the clinical values of qAnti-HBc for differentiating the CHB phase, predicting treatment response, and providing disease prognosis. Moreover, we also discussed the possible mechanism of qAnti-HBc regulation during different courses of HBV infection. [ABSTRACT FROM AUTHOR]