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Simple Summary: In this study, after conducting a comprehensive review of 1356 papers that evaluated the diagnostic performance of deep learning (DL) methods based on medical images for hepatocellular carcinoma (HCC), the findings showed a pooled sensitivity of 89% (95% CI: 87–91), a specificity of 90% (95% CI: 87–92), and an AUC of 0.95 (95% CI: 0.93–0.97). In addition, both the DL methods and human clinicians demonstrated similar levels of performance in HCC detection, with receiver operating characteristic curve (ROC) values of 0.97 (95% CI: 0.95–0.98) for both groups, indicating no discernible difference. Although the heterogeneity was obvious, the utilization of DL methods for diagnosing HCC through medical images has shown promising outcomes. (1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field. [ABSTRACT FROM AUTHOR]

Simple Summary: There are no effective treatments for patients with cancers that induce airway narrowing via extrinsic pressure to the bronchus (i.e., extrinsic malignant central airway obstruction—MCAO). The effects of these cancerous tumors must be quickly alleviated to allow normal breathing and delay disease progression. Currently, stents are used to keep the airway open, but stents do not halt the progression of the cancerous tumor that can crush the stent. We have shown that interstitial photodynamic therapy (I-PDT) can be a safe and beneficial treatment option for patients with extrinsic MCAO. Image-based pre-treatment planning is critical for patient safety and tumor response in I-PDT. Herein, we present and validate novel image-based computer optimization methods for guiding light administration in I-PDT of extrinsic MCAO, based on a rate-based light dose metric. We demonstrate the benefit of our approach in data from representative patients with extrinsic MCAO who were treated with I-PDT. There are no effective treatments for patients with extrinsic malignant central airway obstruction (MCAO). In a recent clinical study, we demonstrated that interstitial photodynamic therapy (I-PDT) is a safe and potentially effective treatment for patients with extrinsic MCAO. In previous preclinical studies, we reported that a minimum light irradiance and fluence should be maintained within a significant volume of the target tumor to obtain an effective PDT response. In this paper, we present a computational approach to personalized treatment planning of light delivery in I-PDT that simultaneously optimizes the delivered irradiance and fluence using finite element method (FEM) solvers of either Comsol Multiphysics® or Dosie™ for light propagation. The FEM simulations were validated with light dosimetry measurements in a solid phantom with tissue-like optical properties. The agreement between the treatment plans generated by two FEMs was tested using typical imaging data from four patients with extrinsic MCAO treated with I-PDT. The concordance correlation coefficient (CCC) and its 95% confidence interval (95% CI) were used to test the agreement between the simulation results and measurements, and between the two FEMs treatment plans. Dosie with CCC = 0.994 (95% CI, 0.953–0.996) and Comsol with CCC = 0.999 (95% CI, 0.985–0.999) showed excellent agreement with light measurements in the phantom. The CCC analysis showed very good agreement between Comsol and Dosie treatment plans for irradiance (95% CI, CCC: 0.996–0.999) and fluence (95% CI, CCC: 0.916–0.987) in using patients' data. In previous preclinical work, we demonstrated that effective I-PDT is associated with a computed light dose of ≥45 J/cm2 when the irradiance is ≥8.6 mW/cm2 (i.e., the effective rate-based light dose). In this paper, we show how to use Comsol and Dosie packages to optimize rate-based light dose, and we present Dosie's newly developed domination sub-maps method to improve the planning of the delivery of the effective rate-based light dose. We conclude that image-based treatment planning using Comsol or Dosie FEM-solvers is a valid approach to guide the light dosimetry in I-PDT of patients with MCAO. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with advanced non-small cell lung cancer (NSCLC) with epidermal growth factor receptor gene (EGFR) Exon 20 insertion mutations (Exon20ins) used to have poor outcomes after the failure of platinum-based therapies. Amivantamab, a drug that targets two proteins involved in NSCLC (EGFR/MET), was developed and studied in the CHRYSALIS clinical trial. This paper summarizes an analysis comparing outcomes for amivantamab from CHRYSALIS to the outcomes for a mix of treatments that were used in real-world clinical practice (real-world physician's choice [RWPC]). RWPC data were collected from hospitals across Europe in the CATERPILLAR real-world evidence (RWE) study. The outcomes of 114 patients from CHRYSALIS were compared to 55 lines of treatment (LOTs) from CATERPILLAR-RWE, accounting for differences in patient characteristics between the two populations. Overall, the results show that amivantamab was at least twice as good as RWPC for platinum pre-treated patients in terms of tumor response and survival rates. Patients with advanced non-small cell lung cancer (NSCLC) with epidermal growth factor receptor gene (EGFR) Exon 20 insertions (Exon20ins) at the second line and beyond (2L+) have an unmet need for new treatment. Amivantamab, a bispecific EGFR- and MET-targeted antibody, demonstrated efficacy in this setting in the phase 1b, open-label CHRYSALIS trial (NCT02609776). The primary objective was to compare the efficacy of amivantamab to the choices made by real-world physicians (RWPC) using an external control cohort from the real-world evidence (RWE) chart review study, CATERPILLAR-RWE. Adjustment was conducted to address differences in prognostic variables between cohorts using inverse probability weighting (IPW) and covariate adjustments based on multivariable regression. In total, 114 patients from CHRYSALIS were compared for 55 lines of therapy from CATERPILLAR-RWE. Baseline characteristics were comparable between the amivantamab and IPW-weighted RWPC cohorts. For amivantamab versus RWPC using IPW adjustment, the response rate ratio for the overall response was 2.14 (p = 0.0181), and the progression-free survival (PFS), time-to-next-treatment (TTNT) and overall survival (OS) hazard ratios (HRs) were 0.42 (p < 0.0001), 0.47 (p = 0.0063) and 0.48 (p = 0.0207), respectively. These analyses provide evidence of clinical and statistical benefits across multiple outcomes and adjustment methods, of amivantamab in platinum pre-treated patients with advanced NSCLC harboring EGFR Exon20ins. These results confirm earlier comparisons versus pooled national registry data. [ABSTRACT FROM AUTHOR]

Simple Summary: Large intestinal cancer is one of the most common lifestyle-related types of cancer. However, the exact mechanism in the relation between adipose tissue, systemic inflammation and intestinal cancer remains unknown. Furthermore, there are many hypotheses regarding the pathways in which alcohol consumption, smoking, malnutrition and physical inactivity cause low-grade systemic inflammation. In this paper, we discuss the relation of lifestyle with inflammation, in terms of inflammatory markers at the time of diagnosis in intestinal cancer patients. Our findings suggest that an overall unhealthier lifestyle and a higher risk of malnutrition at the time of diagnosis were associated with elevated levels of inflammatory markers. These findings could contribute to formulating lifestyle advice in the future. Colorectal cancer is one of the most common lifestyle-related types of cancer. The exact pathophysiologic mechanism in the relation between (visceral) adipose tissue, systemic inflammation and colorectal cancer remains unknown. This study aimed to assess the association of lifestyle with markers of systemic inflammation at the time of diagnosis in stage I-III colorectal cancer patients. Patients (n = 298) with stage I-III colorectal cancer from three Dutch hospitals were included at diagnosis. Several lifestyle-related variables (MUST nutritional status score, WCRF/AICR healthy lifestyle score, active smoking, alcohol consumption and BMI) and inflammatory markers (plasma levels of IL-6, IL-8, IL-10, TNFα and 'high sensitive' hsCRP) were measured at the time of diagnosis. Confounder-adjusted multivariable linear regression models were used to analyse how the lifestyle variables were associated with the inflammatory markers. Statistically significant associations were found between a better WCRF/AICR lifestyle score and lower levels of IL-6 and hsCRP. A medium and high risk of malnutrition according to the MUST score was associated with elevated levels of both IL-8 and hsCRP. An overall unhealthier lifestyle indicated by a lower WCRF/AICR lifestyle score and a higher risk of malnutrition according to the MUST score at the time of diagnosis was associated with elevated levels of inflammatory markers. These findings can contribute to formulating lifestyle advice to improve treatment outcomes and prognosis in patients having CRC in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Various genomic variants that are statistically associated with breast cancer (BC) have been discovered and robustly replicated as a result of different genome-wide association studies. Such findings have led to the development of a different risk classification with the Polygenic Risk Score (PRS). In this paper, we have calculated the PRS of the Norwegian samples using various PRS models, compared their performances, and then evaluated the PRS-based lifetime risk of developing BC. The best performing PRS model includes 3820 SNPs (AUC = 0.625 and OR = 1.567), and the other studied models also provide closer performances. The results show that the PRS can be a useful instrument for lifetime risk stratification of developing BC in the Norwegian population, and can thus be utilized in the BC screening program. Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway. [ABSTRACT FROM AUTHOR]

Simple Summary: The initiation of non-small-cell lung cancer (NSCLC) causes significant changes in a patient's blood plasma metabolomic profile. Locally and early-advanced NSCLC patients receive a complete surgical resection of the lung tumor, but the level of metabolic changes after this surgical treatment is currently unknown. By collecting multiple blood plasma samples before and after complete NSCLC removal, metabolic changes can be detected by analyzing the patient's plasma using proton nuclear magnetic resonance (NMR) spectroscopy. Detection of significant changes in the plasma metabolism, a so-called metabolic shift in the patient's blood plasma after surgical tumor resection, indicates the absence of disease recurrence and thus can provide an indication of a good prognosis. Background: Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor's presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer antigen 125 (CA-125) is a protein found at a high concentration in the blood of patients with specific types of cancer, mainly ovarian cancer. In 2004, the Gynecologic Cancer Intergroup (GCIG) proposed criteria defining response to treatment, as well as disease progression, based on the CA-125 concentration. Ever since, for the follow-up of ovarian cancer patients, the CA-125 concentration and/or CT-scans are used. This paper aims to compare different summaries of CA-125 evolution in the 3 to 6 months following treatment initiation in newly diagnosed advanced ovarian cancer and explore their prognostic capacity to predict overall survival. Based on individual patient data from the GCIG meta-analysis, we propose the most appropriate timeframe between follow-up and the prediction horizon in order to obtain robust, dynamic, individual predictions. (1) Background: Cancer antigen 125 (CA-125) is a protein produced by ovarian cancer cells that is used for patients' monitoring. However, the best ways to analyze its decline and prognostic role are poorly quantified. (2) Methods: We leveraged individual patient data from the Gynecologic Cancer Intergroup (GCIG) meta-analysis (N = 5573) to compare different approaches summarizing the early trajectory of CA-125 before the prediction time (called the landmark time) at 3 or 6 months after treatment initiation in order to predict overall survival. These summaries included observed and estimated measures obtained by a linear mixed model (LMM). Their performances were evaluated by 10-fold cross-validation with the Brier score and the area under the ROC (AUC). (3) Results: The estimated value and the last observed value at 3 months were the best measures used to predict overall survival, with an AUC of 0.75 CI 95% [0.70; 0.80] at 24 and 36 months and 0.74 [0.69; 0.80] and 0.75 [0.69; 0.80] at 48 months, respectively, considering that CA-125 over 6 months did not improve the AUC, with 0.74 [0.68; 0.78] at 24 months and 0.71 [0.65; 0.76] at 36 and 48 months. (4) Conclusions: A 3-month surveillance provided reliable individual information on overall survival until 48 months for patients receiving first-line chemotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Papillary thyroid cancer is not an aggressive cancer, even when metastasis are present; therefore, treatment has been downgraded in recent years. However rare, mortality exists and finding the factors associated with mortality is essential and has not yet been fully accomplished; the answer probably lies in molecular factors. This study aimed to find MicroRNAs related to death in metastatic patients and found that patients who died due to progression of PTC had higher expression levels of miR-101-3p, miR-17-5p, and miR-191-5p compared with those of patients with stable metastatic disease. These findings are new in the literature and may open new doors in differentiating the patients who need more aggressive treatment from those who do not. Papillary thyroid carcinoma (PTC) is the most common neoplasm of the endocrine system and has an excellent long-term prognosis, with low rates of distant metastatic disease. Although infrequent, there are cases of deaths directly related to PTC, especially in patients with metastatic disease, and the factors that could be associated with this unfavorable outcome remain a major challenge in clinical practice. Recently, research into genetic factors associated with PTC has gained ground, especially mutations in the TERT promoter and BRAF gene. However, the role of microRNAs remains poorly studied, especially in those patients who have an unfavorable outcome at follow-up. This paper aims to evaluate molecular markers related to the different pathological processes of PTC, as well as the histological characteristics of the neoplasm, and to compare this profile with prognosis and death from the disease using an analysis of patients treated for metastatic disease in a single tertiary cancer center. Evaluation of microRNA expression in paraffin-embedded tumor specimens was carried out by quantitative PCR using the TaqMan® Low Density Array (TLDA) system. Metastatic patients who died from progression of PTC had higher expressions of miR-101-3p, miR-17-5p, and miR-191-5p when compared to patients with stable metastatic disease. These findings are of great importance but should be considered as preliminary because of the small sample. [ABSTRACT FROM AUTHOR]