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Policy makers and insurers promote the use of generic drugs because they can deliver large savings without sacrificing quality. But these efforts meet resistance from the public, who perceive generic drugs as inferior substitutes for brand name counterparts. Building on literature showing that negative emotions reduce risk-taking, the authors hypothesize that receiving bad medical news (i.e., negative information about one's health) prompts patients to favor brand name over generic drugs as means to safeguard their health. The evidence exploits low-density lipoprotein cholesterol test results, where a discontinuity from clinical guidelines enables the authors to estimate the causal effect of bad medical news. Using data covering patients' prescription drug choices across drug classes, the authors find that patients receiving bad medical news become 8% more likely to choose the brand name alternative. The findings are reinforced by a secondary analysis incorporating the similar context of hemoglobin A1c (blood sugar) testing. The authors also find that bad medical news reduces preferences for generics most strongly among drugs of direct clinical relevance for each test, but the effect also manifests among non–clinically relevant drugs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rural residents have a higher prevalence of colorectal cancer (CRC) mortality compared to urban individuals. Policies have been aimed at improving access to CRC screening to reduce these outcomes. However, little attention has been paid to other determinants of CRC-related outcomes, such as stage at diagnosis, treatment, or survivorship care. The main objective of this analysis was to evaluate literature describing differences in CRC screening, stage at diagnosis, treatment, and survivorship care between rural and urban individuals. MATERIALS AND METHODS: We conducted a systematic review of electronic databases using a combination of MeSH and free-text search terms related to CRC screening, stage at diagnosis, treatment, survivorship care, and rurality. We identified 921 studies, of which 39 were included. We assessed methodological quality using the ROBINS-E tool and summarized findings descriptively. A meta-analysis was performed of studies evaluating CRC screening using a random-effects model. RESULTS: Seventeen studies reported disparities between urban and rural populations in CRC screening, 12 on treatment disparities, and 8 on staging disparities. We found that rural individuals were significantly less likely to report any type of screening at any time period (pooled odds ratio = 0.81, 95% CI, 0.76-0.86). Results were inconclusive for disparities in staging at diagnosis and treatment. One study reported a lower likelihood of use of CRC survivorship care for rural individuals compared to urban individuals. CONCLUSION: There remains an urgent need to evaluate and address CRC disparities in rural areas. Investigators should focus future work on assessing the quality of staging at diagnosis, treatment, and survivorship care in rural areas.

Nanozymes, as an emerging class of enzyme mimics, have attracted much attention due to their adjustable catalytic activity, low cost, easy modification, and good stability. Researchers have made great efforts in developing and applying high-performance nanozymes. Recently, transition-metal-based nanozymes have been designed and widely developed because they possess unique photoelectric properties and high enzyme-like catalytic activities. To highlight these achievements and help researchers to understand the research status of transition-metal-based nanozymes, the development of transition-metal-based nanozymes from material characteristics to biological applications is summarized. Herein, we focus on introducing six categories of transition-metal-based nanozymes and highlight their progress in biomarker sensing and catalytic therapy for tumors. We hope that this review can guide the further development of transition-metal-based nanozymes and promote their practical applications in cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Background: Substance misuse is common among cancer survivors and can negatively impact cancer outcomes. Methods: We conducted a cross-sectional study using National Survey on Drug Use and Health data for 2015 to 2020. We included adult respondents with a history of solid tumor cancer. We calculated the weighted prevalence and corresponding SEs (both expressed as percentages) of substance (alcohol, opioid, sedative, stimulant, other) misuse for respondents with any history of solid tumor cancer and, in secondary analyses, respondents diagnosed with cancer in the prior 12 months. Results: The study included 6,101 respondents with any history of cancer, 1,437 diagnosed in the prior 12 months. Alcohol was the most commonly misused substance. The average prevalence of alcohol misuse was 14.4% (SE 0.60%) across cancer types; it was markedly more common among people with a history or cervical (24.2% [3.0%]) or head and neck cancer (27.4% [7.1%]). The next most common form of substance misuse was opioid misuse (average prevalence: 2.7% [0.25%]). As with alcohol misuse, the prevalence of opioid misuse was higher among those with a lifetime history of cervical cancer (5% [1%]) or head and neck cancer (5% [3%]). Results were generally consistent among cancer survivors diagnosed in the prior 12 months. Conclusions: There is a clear opportunity to address substance misuse—particularly alcohol misuse—among cancer survivors. Such efforts should focus on populations with a high prevalence of substance misuse (e.g., cervical and head and neck cancer survivors) and have strong potential to improve cancer-specific and overall health outcomes. [ABSTRACT FROM AUTHOR]

Summary This study aims to examine and compare the effectiveness of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) and Digital Subtraction Angiography (DSA) in evaluating hypervascular spinal metastases. A comprehensive literature review was conducted, utilizing top‐tier databases such as PubMed, Scopus and Google Scholar, to compile an authoritative and up‐to‐date overview of the current advancements in the field. We synthesized key studies focusing on the advantages, limitations and efficacy of both imaging techniques. DCE‐MRI provides a non‐invasive method for evaluating tissue morphology, perfusion and vascularity, offering valuable information for cancer diagnosis and treatment monitoring. In contrast, DSA is an invasive procedure primarily used for embolization and diagnosing cerebrovascular events. Both modalities have distinct features regarding image acquisition, contrast agents, resolution and accessibility. DCE‐MRI shows promise for cancer‐related applications, offering advantages over conventional MRI by incorporating anatomical and hemodynamic parameters. While DSA remains important for cases requiring critical vascular information, further research is necessary to explore its potential therapeutic benefits in assessing vessel patency. Continued investigations are crucial to uncover additional insights and therapeutic applications for both DCE‐MRI and DSA in medical imaging. [ABSTRACT FROM AUTHOR]

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Background: Individual perceptions, socio-cultural beliefs and health system factors are key determinants of people's health seeking behavior and are widely cited as the causes of delayed breast cancer diagnosis among women from structurally vulnerable settings. Asking: "how do women with a non-lived experience of cancer understand the disease and, what informs their health seeking behaviors?", we qualitatively explored, individual, sociocultural and health system elements from a conceptual model derived from the Socioecological, Health Belief and Cancer Stigma Frameworks, to understand perspectives of breast cancer in a South African urban community setting. Methods: Using a deductive approach and allowing new themes to emerge inductively, we investigated phenomenologically, breast cancer perceptions among 34 women from Soweto, Johannesburg (aged 35–74 years) in 6 Focus Group Discussions. We then conducted 20 follow-up semi-structured in-depth interviews to explore novel themes and suggestions for increasing breast cancer screening. Results: Findings revealed some awareness of breast and other cancers, but confusion and gaps in understanding of the disease, resulting in socio-culturally influenced misperceptions of risks, causes, and outcomes following treatment of breast cancer. This fueled perceptions of profound fear and stigma against people with breast and other cancers. These findings together with participant perceptions of primary healthcare providers being unwelcoming, under-resourced, and insufficiently trained to deal with breast cancer, resulted in women reporting being reluctant to participating in screening/early detection care seeking behavior. Women only accessed primary care when experiencing extreme pain or ill-health. Participants suggested as solutions for future interventions, the need for sustained community engagement, harnessing existing clinic and community stakeholders and resources to provide clear and understandable breast cancer information and encouragement for screening uptake. Conclusions: Health literacy gaps surrounding breast cancer fuels socio-culturally influenced misperceptions, fear, stigma, and fatalism among women from Soweto. Women perceive primary care providers of having insufficient knowledge, skills, and resources to provide effective breast cancer screening services. Participants suggested the need for greater community engagement involving primary clinics and existing community stakeholders working collaboratively. Clear, understandable, and consistent information about breast cancer must be regularly disseminated and communities must be regularly encouraged to utilize breast cancer screening services. [ABSTRACT FROM AUTHOR]

Background: Over the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use. Preoperative APBI, especially with MRI guidance, offers improved tumor targeting and potentially reduced side effects. Magnetic Resonance Imaging-Guided Single-Fraction Pre-Operative Radiotherapy for Early-Stage Breast Cancer (RICE trial) aims to assess the feasibility and efficacy of MRI-guided single-dose radiotherapy (RT) for early-stage breast cancer. Methods: The RICE study is a prospective, single-arm study evaluating single-fraction preoperative, APBI treatment for patients with early-stage breast cancer using a magnetic resonance imaging linear accelerator (MRI linac). Eligible patients enrolled in this study will have a core biopsy to confirm estrogen receptor-positive and HER2-negative sub-type. RT planning will use a planning computed tomography (CT) co-registered with a MRI with the patient in either the supine or prone position. For the diagnostic workup, [18F] fluorodeoxyglucose positron emission tomography/CT ([18F] FDG PET/CT) and [18F] fluoroestradiol positron emission tomography/CT ([18F] FES PET/CT) will be performed prior to treatment. Thirty patients will receive a single ablative RT dose of 21 Gray to the tumor. Pre-treatment and post-treatment MRI scans will be acquired at baseline and 5 weeks post-RT respectively. Breast-conserving surgery will be scheduled for 6 weeks after APBI treatment using the MRI linac. The primary study endpoint is the successful administration of a single fraction of preoperative breast RT under the guidance of an MRI linac. Secondary endpoints include evaluating the utility of MRI, [18F] FDG PET/CT, and [18F] FES PET/CT as a non-invasive method for assessing treatment response in patients undergoing single-fraction preoperative APBI. Conclusion: The RICE trial represents a significant step in breast cancer treatment, offering insights that could lead to treatment protocols with minimized RT appointments and enhanced patient outcomes. Trial registration: This trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR). Registered 31st of May 2021. Registration number: ACTRN12621000659808. [ABSTRACT FROM AUTHOR]

The diagnosis and treatment of ovarian cancer (OC) are still a grand challenge, more than 70% of patients are diagnosed at an advanced stage with a dismal prognosis. Magnetic resonance imaging (MRI) has shown superior results to other examinations in preoperative assessment, while cisplatin‐based chemotherapy is the first‐line treatment for OC. However, few previous studies have brought together the two rapidly expanding fields. Here a technique is presented using cisplatin prodrug (Pt‐COOH), Fe3+, and natural polyphenols (Gossypol) to construct the nanoparticles (HA@PFG NPs) that have a stable structure, controllable drug release behavior, and high drug loading capacity. The acidic pH values in tumor sites facilitate the release of Fe3+, Pt‐COOH, and Gossypol from HA@PFG NPs. Pt‐COOH with GSH consumption and cisplatin‐based chemotherapy plus Gossypol with pro‐apoptotic effects displays a synergistic effect for killing tumor cells. Furthermore, the release of Fe3+ at the tumor sites promotes ferroptosis and enables MRI imaging of OC. In the patient‐derived tumor xenograft (PDX) model, HA@PFG NPs alleviate the tumor activity. RNA sequencing analysis reveals that HA@PFG NPs ameliorate OC symptoms mainly through IL‐6 signal pathways. This work combines MRI imaging with cisplatin‐based chemotherapy, which holds great promise for OC diagnosis and synergistic therapy. [ABSTRACT FROM AUTHOR]

Clinical breast examination (CBE) screening for breast cancer in Vietnam was proved to be cost‐effective and acceptable to women. However, the outcomes of the implementation of such proposed programme are unknown. To inform the design and implementation planning of the proposed screening programme, this study investigated and analysed the contextual factors of implementing a CBE programme as part of primary care in Vietnam. Regarding methods, the Breast Health Global Initiative Framework and its seven contextual domains for breast cancer health care (including i. epidemiological, ii. policy/legal, iii. geographical/environmental, iv. service and organisational, v. financial, vi. social and economic, vii. cultural and psychosocial) guided the programme implementation analysis. Literature review, documentary analysis of polices, and key informant interviews (with representatives of health authorities at all four levels of Vietnam health system) were used to gather data for each domain. The results show that Vietnam's policy context pointed to the focus on cancer early detection through screening. According to key informants, district level was the most appropriate level for organising and delivering a CBE programme, and only doctors should perform a CBE. Knowledge about breast cancer and perceived susceptibility to receiving a breast cancer diagnosis was positively associated with CBE uptake. In conclusions, the current policy framework supports the implementation of a CBE screening programme in Vietnam. CBE should be performed by doctors, at the district level, and alongside health promotion. [ABSTRACT FROM AUTHOR]

Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key Points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features. [ABSTRACT FROM AUTHOR]

The catalytic hairpin assembly (CHA)-based miRNA detection methods have garnered significant attention due to their simplicity and acceptable amplification efficiency. However, these methods necessitate improved sensitivity. In this work, we present a colorimetric and ultrasensitive approach for the detection of cancer-related miRNAs which is initiated by CHA-mediated nicking endonuclease-assisted signals recycling. The target initiates the CHA process to expose the functional section in the P2 probe. This section can activate cascaded recycling cycles to produce numerous linker sequences by Nt.AlwI endonuclease-assisted cleavage of two hairpin signal probes. The 3,3',5,5'-tetramethylbenzidine sulfate (TMB)/H2O2-based color reaction is induced by the fixation of the cDNA-HRP on the surface of magnetic beads, which is mediated by the linker sequence. The proposed method demonstrates a sensitivity that is either comparable to or superior to that of previous colorimetric miRNA detection methods, as a result of this design. Furthermore, the method demonstrated a promising potential for clinical applications and a high selectivity to target miRNA. Consequently, it provides a colorimetric assay that is both ultrasensitive and dependable for the visual detection of miRNA, which has the potential to revolutionize the early diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Background: Although suspicions of cancer may be raised in patients who visit the emergency department, little is known about emergency department use before a cancer diagnosis. We sought to describe emergency department use among patients in Ontario within the 90 days before confirmed cancer diagnosis and to evaluate factors associated with this emergency department use. Methods: We conducted a retrospective, population-based study of patients aged 18 years or older who had a confirmed cancer diagnosis in Ontario from 2014 to 2021 using linked administrative databases. The primary outcome was any emergency department visit within 90 days before the cancer diagnosis date. We used multivariable logistic regression to evaluate factors associated with emergency department use, such as demographics (e.g., age, sex, rurality, Ontario Health region, indicators of marginalization), comorbidities, previous emergency department visits and hospital admissions, continuity of primary care, type of cancer, and year of cancer diagnosis. Results: We included 651 071 patients with cancer. Of these, 229 683 (35.3%) had an emergency department visit within 90 days before diagnosis, 51.4% of whom were admitted to hospital from the emergency department. Factors associated with increased odds of emergency department use before cancer diagnosis included rurality (odds ratio [OR] 1.15, 95% confidence interval [CI] 1.13–1.17), residence in northern Ontario (North East region OR 1.14, 95% CI 1.10–1.17 and North West region OR 1.27, 95% CI 1.21–1.32, v. Toronto region), and living in the most marginalized areas (material resources OR 1.37, 95% CI 1.35–1.40 and housing OR 1.09, 95% CI 1.06–1.11, v. least marginalized quintile). We observed significant variation in emergency department use by cancer type, with high odds of emergency department use among patients with intracranial, pancreatic, liver or gallbladder, or thoracic cancer. Interpretation: Emergency department use is common before cancer diagnosis, with about one-third of patients with cancer in Ontario using the emergency department before diagnosis. Understanding why patients visit the emergency department before cancer diagnosis is important, particularly for patients who live in rural or marginalized areas, or those who have specific cancer types. [ABSTRACT FROM AUTHOR]

AbstractThis study’s purpose is to determine the effect of mindfulness-based psycho-education program for parents of children with leukemia on ruminative thought, perceived stress, and well-being. This experimental randomized controlled study was adopted with the parents of children. Mindfulness-based psychoeducation program was applied on online platform with parents. After the intervention, ruminative thinking styles, perceived stress levels and well-being levels of the parents in the experimental group were found to be significantly different from the control group (p < 0.05). The mindfulness-based psychoeducation program applied in this study was found to be effective in reducing ruminative thinking and perceived stress in parents of children diagnosed with leukemia. It was also concluded that the program was effective in maintaining parents’ level of well-being. [ABSTRACT FROM AUTHOR]

ABSTRACT Kaposi sarcoma is a human herpesvirus 8–associated angio‐proliferative tumor arising from lymphatic endothelial cells. Four clinical subtypes are known: classic, epidemic, endemic, and iatrogenic. The development of Kaposi sarcoma and lymphedema may be interlinked, where each condition could potentially support the progression of the other. Post‐mastectomy lymphedema is a commonly recognized complication following radical mastectomy. Angiosarcoma is the most frequently reported neoplasm in such a situation. We present a 72‐year‐old female who developed Kaposi sarcoma on the same side of mastectomy 9 years following her initial diagnosis and treatment for cancer breast. The diagnosis of Kaposi sarcoma was based on the histopathologic findings and was confirmed with immunohistochemical staining for human herpes virus 8 and D2‐40. Lymphedema may be associated with local immune suppression manifested in the form of defective cell–mediated immunity and antigen‐presenting cell migration defect which may facilitate development of neoplasms. It is important to differentiate Kaposi sarcoma from other vascular tumors which may have a much worse prognosis. Patients with lymphedema should receive appropriate management and undergo long‐term follow‐up for early detection of any potential malignancies. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is a common pregnancy complication characterized by insulin resistance. A link has been suggested between insulin resistance and breast cancer, which is the most common cancer in women. Hence, women with previous GDM may be at increased risk of developing breast cancer, yet, the existing evidence is conflicting. This study explored the association between GDM and incident breast cancer, including age at cancer diagnosis. Additionally, we investigated the potential impact of severity of insulin resistance during pregnancy and of subsequent diabetes development on the breast cancer risk. Methods: We conducted a nationwide, register-based cohort study including all women giving birth in Denmark from 1997 to 2018. We defined GDM and breast cancer based on ICD-10 codes. Premenopausal and postmenopausal breast cancer was pragmatically defined as age at outcome < 50 years and ≥ 50 years, respectively. A proxy for severity of insulin resistance during pregnancy was based on insulin treatment; subsequent diabetes was defined as presence of ICD-10 codes and/or antidiabetic medication after pregnancy. The statistical analyses included Cox regression, logistic regression and t-test. Results: Of 708,121 women, 3.4% had GDM. The median follow-up period was 11.9 years (range 0-21.9). The overall breast cancer risk was comparable in women with and without previous GDM (adjusted hazard ratio 0.96 [95% CI 0.83–1.12]). Premenopausal and postmenopausal breast cancer risk also did not differ; however, women with previous GDM had a breast cancer diagnosis at younger age (42.6 vs. 43.5 years, p-value 0.01). All-cause mortality was similar regardless of GDM history. Severity of insulin resistance during pregnancy and subsequent diabetes did not affect breast cancer risk. Conclusions: This large, population-based cohort study showed no higher risk of incident breast cancer in women with previous GDM compared to women without previous GDM after a median of almost 12 years of follow-up. This was evident irrespective of menopausal state. The breast cancer risk was not influenced by the severity of insulin resistance during pregnancy and by subsequent diabetes development. Regardless of GDM history, attention towards prevention, early detection and treatment of breast cancer should be prioritized. [ABSTRACT FROM AUTHOR]

Background With aging of the population and improvements in diagnosis, treatment, and supportive care, the number of cancer survivors in the United States has increased; updated prevalence estimates are needed. Methods Cancer prevalence on January 1, 2022, was estimated using the Prevalence Incidence Approach Model, utilizing incidence, survival, and mortality. Prevalence by age decade, sex, and time from diagnosis was calculated. The percentage of cancer survivors in the projected US population by age and sex was calculated as the ratio of the sex-specific projected prevalence to the sex-specific projected US population. Results There were an estimated 18.1 million US cancer survivors as of January 1, 2022. From 2022 to 2030, the number of US cancer survivors is projected to increase to 21.6 million; by 2040, the number is projected to be 26 million. Long-term survivors are highly prevalent; in 2022, 70% of cancer survivors had lived 5 years or more after diagnosis, and 11% of cancer survivors had lived 25 years or more after diagnosis. Among all US females aged 40-54 years, 3.6% were cancer survivors; among females aged 65-74 years, 14.5% were cancer survivors; among females aged 85 years and older, 36.4% were cancer survivors. Among all US males aged 40-54 years, 2.1% were cancer survivors; among males aged 65-74 years, 16% were cancer survivors; and among those aged 85 years and older, 48.3% were cancer survivors. Conclusions Cancer survivors are growing in number. In the United States, most cancer survivors are long-term and very long-term survivors, representing a substantial proportion of the US population. [ABSTRACT FROM AUTHOR]

In the contemporary landscape of healthcare, machine learning models are pivotal in facilitating precise predictions, particularly in the nuanced diagnosis of complex ailments such as breast cancer. Traditional diagnostic methodologies grapple with inherent challenges, including excessive complexity, elevated costs, and reliance on subjective interpretation, which frequently culminate in inaccuracies. The urgency of early detection cannot be overstated, as it markedly broadens treatment modalities and significantly enhances survival rates. This paper delineates an innovative optimization framework designed to augment diagnostic accuracy by amalgamating momentum-based optimization techniques within a neural network paradigm. Conventional machine learning approaches are often encumbered by issues of overfitting, data imbalance, and the inadequacy of capturing intricate patterns in high-dimensional datasets. To counter these limitations, we propose a sophisticated framework that integrates an adaptive threshold mechanism across an array of gradient-based optimizers, including SGD, RMSprop, adam, adagrad, adamax, adadelta, nadam and Nesterov momentum. This novel approach effectively mitigates oscillatory behavior, refines parameter updates, and accelerates convergence. A salient feature of our methodology is the incorporation of a momentum threshold for early stopping, which ceases training upon the stabilization of momentum below a pre-defined threshold, thereby pre-emptively preventing overfitting. Leveraging the Wisconsin Breast Cancer Dataset, our model achieved a remarkable 99.72% accuracy and 100% sensitivity, significantly curtailing misclassification rates compared to traditional methodologies. This framework stands as a robust solution for early breast cancer diagnosis, thereby enhancing clinical decision making and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM's potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40–65 who underwent DM with DBT at King Khaled University Hospital from 2020–2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether ADC values could be used to establish the aggressiveness of prostate cancer. Methods: A retrospective single-center study included 398 patients with prostate cancer who underwent a multiparametric MRI prior to radical prostatectomy. DWI ADC values were measured (µm2/s) using b values of 50 and 1000. The dominant lesion best visualized on MRI was analyzed. The ADC values of the index lesion and reference tissue were compared to tumor aggressivity according to the Gleason grade groups based on radical prostatectomy results. Statistical analysis was performed using the Mann–Whitney U test, Kruskal–Wallis H test, Spearman's rank correlation, and ROC curves. Results: A very strong negative correlation (rs = −0.846, p < 0.001) between ADC and GS was found. ROC analysis revealed an AUC of 0.958 and an ADC threshold value of 758 µm2/s in clinically significant prostate cancer diagnoses using the absolute ADC value, with no advantage of using the ADC ratio over the absolute ADC value being identified. Conclusion: DWI ADC values and the calculated ADC ratio have a significant inverse correlation with GS. The findings indicate a strong capability in determining prostate cancer aggressiveness, as well as the possibility of assisting with assigning PI-RADS categories using ADC as quantitative metrics. [ABSTRACT FROM AUTHOR]

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement. [ABSTRACT FROM AUTHOR]

Head and neck cancers (HNCs) arise from anatomically adjacent sites and subsites, with varying etiological factors, diagnostic strategies, prognoses, and treatment approaches. While conventional squamous cell carcinoma (SCC) is the most common histology in the head and neck district, HNCs encompass a variety of rare histopathological entities, categorized into epithelial tumors such as salivary gland cancers, sinonasal tumors, neuroendocrine tumors, malignant odontogenic tumors, and SCC variants versus non-epithelial tumors including soft tissue sarcomas, mucosal melanomas, and hematological malignancies. Rare HNCs (R-HNCs) represent a diagnostic and clinical challenge, requiring histopathological expertise, the availability of peculiar molecular analysis, and the personalization of local and systemic treatments, all guided by a multidisciplinary tumor board. Here, we provide a comprehensive literature review on R-HNCs, emphasizing key histopathological and molecular characteristics that are crucial for guiding treatment decisions. An insight about the latest developments in systemic treatments is also reported. [ABSTRACT FROM AUTHOR]

Background: Photodynamic diagnosis (PDD)‐assisted transurethral resection of bladder tumors (TURBT) has emerged as a promising complementary tool to white light (WL) cystoscopy, potentially improving cancer detection and replacing conventional mapping biopsies. This study aimed to investigate the diagnostic accuracy of PDD by anatomical locations in mapping biopsies through lesion‐based analysis. Methods: PDD and WL findings were prospectively recorded in 102 patients undergoing mapping biopsies and PDD‐assisted TURBT using oral 5‐aminolevulinic acid. We evaluated 673 specimens collected from flat tumor or normal‐looking lesions on WL cystoscopy, after excluding 98 specimens collected from papillary or nodular tumors. Results: Among the 673 lesions, cancer was detected in 110 (16%) by lesion‐based analysis. PDD demonstrated significantly higher sensitivity (65.5% vs. 46.4%, p < 0.001) and negative predictive value (92.5% vs. 89.5%, p < 0.001) compared to WL. The sensitivity of PDD findings varied by location: posterior (100%), right (78.6%), dome (73.3%), left (70.6%), trigone (58.8%), bladder neck (41.7%), anterior (40.0%), and prostatic urethra (25.0%). Incorporating targeted biopsies of specific locations (bladder neck, anterior, and prostatic urethra) into the PDD‐guided biopsies, regardless of PDD findings, significantly increased the overall sensitivity from 65.5% to 82.7% (p = 0.001). Conclusions: This study first demonstrated the detection rate of location‐specific mapping biopsies using PDD, revealing difficulties in accuracy assessment in areas susceptible to tangential fluorescence. While PDD‐guided biopsy improves cancer detection compared to WL cystoscopy even for flat tumors or normal‐looking lesions, more careful decisions, including mapping biopsies, may be beneficial for an assessment in these tangential areas. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is one of the most important health problems among elderly men in China, with the increasing aging of the population. A cross-sectional survey was conducted in eastern China from 2022 to 2023. Recruitment included a total of 70,342 participants aged 60 or older. Social demographic information, such as individual factors like age, education levels and behavior habits, physical examination and laboratory tests results were collected. Age-standardized prevalence rates were estimated by a direct method with a standard population. T test and chi-square test were used to compare the statistical differences. Multivariate regression models were used to identify the risk factors of PCa. Overall, the crude and age-standardized PCa prevalence is 0.93% and 0.91%, respectively in eastern China. When we adjusted all the co-variables, it showed that high age, smoking, having a higher BMI and higher CA19-9 were associated with a higher risk for a prostate cancer diagnosis. Faced with the demographic transition, innovative strategies are needed to control and prevent PCa. Conducting early screening among male population in community health service centers in eastern of China, especially among a population with associated risk factors such as high age, obesity, and conducting community-based intervention programs might be helpful to address this increasingly significant health problem. [ABSTRACT FROM AUTHOR]

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Purpose: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. Materials and methods: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. Results: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. Conclusion: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. Clinical relevance statement: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. Key Points: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes. [ABSTRACT FROM AUTHOR]

MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. Key Points: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively. [ABSTRACT FROM AUTHOR]

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. Clinical relevance statement: High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. Key Points: High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI. [ABSTRACT FROM AUTHOR]

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management. [ABSTRACT FROM AUTHOR]

Objectives: To assess the risk of venous thromboembolic events (VTEs) and bleeding with or without thromboprophylaxis during neoadjuvant chemotherapy in bladder cancer patients scheduled for radical cystectomy. Materials and Methods: We conducted a retrospective cohort study in 4886 patients with non‐metastatic bladder cancer undergoing cystectomy across 28 centres in 13 countries between 1990 and 2021. Inverse probability weighting analyses were performed to estimate the effect of thromboprophylaxis on VTE and bleeding. Results: In 147 patients (3%) VTEs were recorded within the first year. These occurred a median (interquartile range [IQR]) of 127 (82–198) days after bladder cancer diagnosis. Bleeding events occurred in 131 patients (3%) within the first year. These occurred a median (IQR) of 101 (83–171) days after cancer diagnosis. In inverse probability weighting analyses, compared to patients without thromboprophylaxis during chemotherapy, patients with thromboprophylaxis had not only a lower risk of VTE (hazard ratio [HR] 0.32, 95% confidence interval [CI] 0.12–0.81; P = 0.016) but also a lower bleeding risk (HR 0.03, 95% CI 0.09–0.12; P <0.0001). The retrospective nature of the study was its main limitation. Conclusions: In this retrospective analysis, the benefit of thromboprophylaxis during neoadjuvant chemotherapy before cystectomy is in line with data from randomised trials in other malignancies. Our data suggest thromboprophylaxis is protective against VTEs and should be the standard of care during neoadjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

We aimed to generate a model of cancer-related fatigue (CRF) of clinical importance 2 years after diagnosis of breast cancer building on clinical and behavioral factors and integrating pre-treatment markers of systemic inflammation. Women with stage I-III hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer were included from the multimodal, prospective CANTO cohort (NCT01993498). The primary outcome was global CRF of clinical importance [European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 ≥40/100] 2 years after diagnosis (year 2). Secondary outcomes included physical, emotional, and cognitive CRF (EORTC QLQ-FA12). All pre-treatment candidate variables were assessed at diagnosis, including inflammatory markers [interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, interferon γ, IL-1 receptor antagonist, tumor necrosis factor-α, and C-reactive protein], and were tested in multivariable logistic regression models implementing multiple imputation and validation by 100-fold bootstrap resampling. Among 1208 patients, 415 (34.4%) reported global CRF of clinical importance at year 2. High pre-treatment levels of IL-6 (quartile 4 versus 1) were associated with global CRF at year 2 [adjusted odds ratio (aOR): 2.06 (95% confidence interval [CI] 1.40-3.03); P = 0.0002; area under the receiver operating characteristic curve = 0.74]. Patients with high pre-treatment IL-6 had unhealthier behaviors, including being frequently either overweight or obese [62.4%; mean body mass index 28.0 (standard deviation 6.3 kg/m2)] and physically inactive (53.5% did not meet World Health Organization recommendations). Clinical and behavioral associations with CRF at year 2 included pre-treatment CRF [aOR versus no pre-treatment CRF: 3.99 (95% CI 2.81-5.66)], younger age [aOR per 1-year decrement: 1.02 (95% CI 1.01-1.03)], current tobacco smoking [aOR versus never: 1.81 (95% CI 1.26-2.58)], and worse insomnia or pain [aOR per 10-unit increment: 1.08 (95% CI 1.04-1.13), and 1.12 (95% CI 1.04-1.21), respectively]. Secondary analyses indicated additional associations of IL-2 [aOR per log-unit increment: 1.32 (95% CI 1.03-1.70)] and IL-10 [0.73 (95% CI 0.57-0.93)] with global CRF and of C-reactive protein [1.42 (95% CI 1.13-1.78)] with cognitive CRF at year 2. Emotional distress was consistently associated with physical, emotional, and cognitive CRF. This study proposes a bio-behavioral framework linking pre-treatment systemic inflammation with CRF of clinical importance 2 years later among a large prospective sample of survivors of breast cancer. • 34.4% of 1208 stage I-III breast cancer survivors had cancer-related fatigue of clinical importance 2 years after diagnosis. • High pre-treatment levels of the pro-inflammatory cytokine IL-6 were associated with global fatigue 2 years later. • Individuals with high levels of IL-6 had higher BMI and were less physically active than those with lower levels. • Higher pre-treatment IL-2 and IL-10 were also associated with higher and lower likelihood of global fatigue, respectively. • Higher C-reactive protein was associated with higher likelihood of cognitive fatigue. [ABSTRACT FROM AUTHOR]

With the widespread use of 18F‐fluorodeoxyglucose positron emission tomography (FDG PET/CT) in the investigation and staging of cancers, incidental discovery of FDG‐avid thyroid nodules is becoming increasingly common, with a reported incidence in the range 1%–4% of FDG PET/CT scans. The risk of malignancy in an incidentally discovered FDG avid thyroid nodule is not clear due to selection bias in reported retrospective series but is likely to be less than 15%. Even in cases where the nodule is found to be malignant, the majority will be differentiated thyroid cancers with an excellent prognosis even without treatment. If, due to index cancer diagnosis, age and co‐morbidities, it is unlikely that the patient will survive 5 years, further investigation of an incidental FDG avid thyroid nodule is unlikely to be warranted. We provide a consensus statement on the circumstances in which further investigation of FDG avid thyroid nodules with ultrasound and fine needle aspiration might be appropriate. [ABSTRACT FROM AUTHOR]

Objective: The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. Methods: Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. Results: We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. Conclusion: Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP. [ABSTRACT FROM AUTHOR]

The aim of this meta-analysis was to assess the diagnostic performance of deep learning (DL) and ultrasound in breast cancer diagnosis. Additionally, we categorized the included studies into two subgroups: B-mode ultrasound diagnostic subgroup and multimodal ultrasound diagnostic subgroup, and compared the performance differences of DL algorithms in breast cancer diagnosis using only B-mode ultrasound or multimodal ultrasound. We conducted a comprehensive search for relevant studies published from January 01, 2017 to July 31, 2023 in the MEDLINE and EMBASE databases. The quality of the included studies was evaluated using the QUADAS-2 tool and radiomics quality scores (RQS). Meta-analysis was performed using R software. Inter-study heterogeneity was assessed by I^2 values and Q-test P-values, with sources of heterogeneity analyzed through a random effects model based on test results. Summary receiver operating characteristics (SROC) curves were used for meta-analysis across multiple trials, while combined sensitivity, specificity, and AUC were calculated to quantify prediction accuracy. Subgroup analysis and sensitivity analyses were also conducted to identify potential sources of study heterogeneity. Publication bias was assessed using the funnel plot method. (PROSPERO identifier: CRD42024545758). The 20 studies included a total of 14,955 cases, with 4197 cases used for model testing. Among these cases were 1582 breast cancer patients and 2615 benign or other breast lesions. The combined sensitivity, specificity, and AUC values across all studies were found to be 0.93, 0.90, and 0.732, respectively. In subgroup analysis, the multimodal subgroup demonstrated superior performance with combined sensitivity, specificity, and AUC values of 0.93, 0.88, and 0.787, respectively; whereas the combined sensitivity, specificity, and AUC value for the model B subgroup was at a level of 0.92, 0.91, and 0.642, respectively. The integration of DL with ultrasound demonstrates high accuracy in the adjunctive diagnosis of breast cancer, while the fusion of DL and multimodal breast ultrasound exhibits superior diagnostic efficacy compared to B-mode ultrasound alone. • The application of deep learning in the diagnosis of breast cancer is promising. • Deep learning-based computer-aided detection demonstrates high accuracy. • Multimodal breast ultrasound is expected to enhance the diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system, and its incidence rate ranks ninth in the world. In recent years, the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer. In this study, based on microscopic hyperspectral data, an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed. Support vector machine (SVM) is used to build classification models and compare the classification performance of spectral feature, spectral and shape fusion feature, and the fusion feature proposed in this paper on the same classifier. The results show that the sensitivity, specificity, and accuracy of our classification algorithm based on shape and color fusion features are 0.952, 0.897, and 0.920, respectively, which are better than the classification algorithm only using spectral features. Therefore, this study can effectively extract the cell features of bladder urothelial carcinoma smear, thus achieving automatic, real-time, and noninvasive detection of bladder tumor cells, and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer, and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease. [ABSTRACT FROM AUTHOR]

Secukinumab is a monoclonal antibody directed against interleukin-17 approved for the treatment of psoriasis and spondyloarthritis. The favorable oncological profile of secukinumab in patients with a history of malignancy has been shown in patients with psoriasis. However, systematic data to this regard have not been published yet for patients with spondyloarthritis. The objective of the present study was to evaluate the oncological safety of secukinumab in patients affected by this group of diseases. We performed a retrospective study in which we identified from our cohort patients with spondyloarthritis treated with secukinumab and with a history of malignancy. These patients' baseline demographic, treatment, rheumatological, and oncological data were collected. The neoplastic outcome (i.e., cancer recurrence or progression) after secukinumab start was then analyzed. Our study included 22 patients with spondyloarthritis. The most frequently reported oncological diagnosis was breast cancer (9 [41%] patients). Secukinumab was started after a median of 24 months following cancer diagnosis. At this time point, all but three patients were in oncological remission. No case of cancer relapse or progression was recorded over a median follow-up of 30 months. In the largest cohort reported to date to this regard, secukinumab was not associated with oncological recurrence or progression in patients with spondyloarthritis with a history of malignancy. Secukinumab may, therefore, represent a safe option in this clinical scenario. [ABSTRACT FROM AUTHOR]

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Nanoflowers, an innovative class of nanoparticles with a distinctive flower-like structure, have garnered significant interest for their straightforward synthesis, remarkable stability, and heightened efficiency. Nanoflowers demonstrate versatile applications, serving as highly sensitive biosensors for rapidly and accurately detecting conditions such as diabetes, Parkinson's, Alzheimer's, and foodborne infections. Nanoflowers, with their intricate structure, show significant potential for targeted drug delivery and site-specific action, while also exhibiting versatility in applications such as enzyme purification, water purification from dyes and heavy metals, and gas sensing through materials like nickel oxide. This review also addresses the structural characteristics, surface modification, and operational mechanisms of nanoflowers. The nanoflowers play a crucial role in preventing premature drug leakage from nanocarriers. Additionally, the nanoflowers contribute to averting systemic toxicity and suboptimal therapy efficiency caused by hypoxia in the tumor microenvironment during chemotherapy and photodynamic therapy. This review entails the role of nanoflowers in cancer diagnosis and treatment. In the imminent future, the nanoflowers system is poised to revolutionize as a smart material, leveraging its exceptional surface-to-volume ratio to significantly augment adsorption efficiency across its intricate petals. This review delves into the merits and drawbacks of nanoflowers, exploring synthesis techniques, types, and their evolving applications in cancer. [ABSTRACT FROM AUTHOR]

Purpose: Pregnant women with a diagnosis of differentiated thyroid cancer (DTC) were potentially high-risk but largely ignored study population. We aimed to explore whether gestational thyrotropin levels were associated with progression of DTC. Methods: We conducted a retrospective cohort study at Peking University Third Hospital in Beijing, China from January 2012 to December 2022. We included pregnant women with a pre-pregnancy DTC managed by active surveillance (under-surveillance DTC) or surgical treatment (after-surgery DTC). Dynamic changes of gestational thyrotropin levels across multiple time points were characterized by both statistical (average level, change instability, longitudinal trajectory) and clinical (thyroid dysfunction, thyrotropin suppression, and achievement of thyrotropin suppression target) indicators. Outcomes were clinician-validated progression of DTC, measured separately for patients under surveillance (tumor enlargement or lymph node metastasis) and those after surgery (≥ 3 mm growth in the size of existing metastatic foci, development of new lymph node metastases, ≥ 2 mm growth in the size of existing cancer foci in the contralateral thyroid, or biochemical progression). Results: Among 43 and 118 patients with under-surveillance and after-surgery DTC, we observed no evidence of associations between any of the quantitative or clinical indicators of gestational thyrotropin levels and progression-free survival, after a median of 2.63 (IQR: 0.90-4.73) and 4.22 (2.53-6.02) year follow-up, respectively (all P values > 0.05). Conclusions: Gestational thyrotropin levels appeared to play a minor role in the progression of under-surveillance or after-surgery DTC. Clinicians might focus on the risk of adverse pregnancy outcomes when optimizing thyrotropin levels for pregnant women with a diagnosis of DTC. [ABSTRACT FROM AUTHOR]

Objectives: Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. Methods: In this study, we proposed an artificial intelligence (AI)‐based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast‐XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. Results: Six classifiers, support vector machine (SVM), K‐nearest neighbor (K‐NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast‐XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. Conclusions: The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results. [ABSTRACT FROM AUTHOR]

Breast cancer occurs when cells or tissues of the breast grow abnormally. On a global scale, Globally, cancer is a common and deadly type of disease that most affects women. These studies provide a new paradigm for skillful deep recognition of preprocessed images using deep neural networks. At this stage, the paper presents an efficient BreastNet model for breast cancer detection containing 48 layers, applied to three datasets namely breast ultrasound images (BUST), which includes 780 images classified into benign, malignant, normal and invasive ductal carcinoma (IDC) and BreakHis_v1_400X. Breast cancer images are processed using different feature extraction vectors. Medical image processing is a crucial step in adjusting the brightness and contrast of images. Gamma correction can help preserve detail in the darkest and brightest areas of an image. In addition to its combination of complex-frequency features, Geronimo-Hardin-Massopust (GHM) multiwave conversion is a type of multiwave conversion that offers many advantages for image processing tasks. By capturing low-frequency (soft areas) and high-frequency (edges and textures) components of an image more accurately, Discrete Wavelet Transform (DWT) processing can also In order to represent an image more efficiently, it is necessary to reduce the amount of data required while retaining important visual details. The proposed model, BreastNet, improved the classification accuracy performed on the proposed dataset by 95.88%, 93.30%, and 91.16%. The proposed model has a better execution rate than AlexNet, VGG16, Googlenet, Resnet18, and Resnet50 models. [ABSTRACT FROM AUTHOR]

Palladium nanoparticles (PdNPs) in the past decade have been extensively used in the diagnosis, imaging, and treatment of numerous diseases owing to their unique physical, chemical, and catalytic properties. Previously, Palladium was almost singularly used for catalysis during chemical synthesis in industries. It is only in the past decade that researchers explore its potential in biomedicine. PdNPs can also be used in the imaging and diagnosis of cancer through techniques such as photoacoustic imaging, computed tomography, and flow cytometry-assisted cell sorting. They offer various advantages such as tunability, desirable photothermal and optical properties, thermal stability, antimicrobial properties, and many more. Cancer treatment through conventional techniques and the broad use of cytotoxic therapeutics has reached a plateau. This paved the way to design novel selective and targeted therapies, one of them being PdNPs. PdNPs are most commonly used in synergy with chemotherapy, photothermal therapy, photodynamic therapy, as well as radiotherapy which improves the efficacy of cancer treatment. In this review, several studies are included that summarize the synthesis, characterization, and cytotoxicity of palladium nanoparticles as well as the functionalized PdNP complexes. In addition, the review provides insight into the mechanism of palladium nanoparticles in improving the efficacy and efficiency of synergistic therapies. [ABSTRACT FROM AUTHOR]

Chemodynamic therapy (CDT), designed to trigger a tumor-specific hydrogen peroxide (H2O2) reaction generating highly toxic hydroxyl radicals (·OH), has been investigated for cancer treatment. Unfortunately, the limited Fenton or Fenton-like reaction rate and the significant impact of excessive reducing glutathione (GSH) in the tumor microenvironment (TME) have severely compromised the effectiveness of CDT. To address this issue, we designed a dual-responsive nanoplatform utilizing a metal-polyphenol network (MPN) -coated multi-caged IrOx for efficient anti-tumor therapy in response to the acidic TME and intracellular excess of GSH, in which MPN composed of Fe3+ and tannic acid (TA). Initially, the acidic TME and intracellular excess of GSH lead to the degradation of the MPN shell, resulting in the release of Fe3+ and exposure of the IrOx core, facilitating the efficient dual-pathway CDT. Subsequently, the nanoplatform can mitigate the attenuation of CDT by consuming the excessive GSH within the tumor. Finally, the multi-caged structure of IrOx is advantageous for effectively implementing photothermal therapy (PTT) in coordination with CDT, further enhancing the therapeutic efficacy of tumors. Moreover, the outstanding Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) (T1/T2) multimodal imaging capabilities of IrOx@MPN enable early diagnosis and timely treatment. This work provides a typical example of the construction of a novel multifunctional platform for dual-responsive treatment of tumors. [ABSTRACT FROM AUTHOR]

Purpose: Prostate cancer is the second most diagnosed male malignancy in the U.S. 2,4-Dichlorophenoxyacetic acid (2,4-D) is a commonly used herbicide and potential carcinogen. The researchers evaluated the association between prostate cancer and 2,4-D. Methods: Data was leveraged from the National Health and Nutrition Examination Survey (NHANES), a population-based, cross-sectional study of men and women in the U.S. Our cohort of interest was men aged 50 years and over. Urinary 2,4-D served as the measure of exposure. Our primary outcome was history of prostate cancer based on an individual's reply of "yes" to either ever having been diagnosed with prostate cancer and/or having received treatment for prostate cancer. Chi-square, one-way analysis of variance (ANOVA), and multivariable, weighted logistic regression were used to analyze the relationship between 2,4-D and prostate cancer. Results: We identified 1,788 eligible men, representing an estimated 691,709 men after survey weighting. The median 2,4-D level was 0.28 µg/L (IQR: 0.26–0.53), and the geometric mean was 0.38 µg/L. Increasing exposure of 2,4-D was associated with prostate cancer (OR 1.72, 95% CI [1.2,2.4]). Individuals in the highest quartile of 2,4-D exposure had a higher odds of a prostate cancer diagnosis compared to the lowest quartile (OR = 3.46 95% CI [1.11,10.72]). Age stratification revealed statistically significant associations between 2,4-D and prostate cancer in men age 70 + who were in the highest quartile of exposure (OR = 3.79 95% CI [1.51,9.52]). Conclusions: These findings implicate 2,4-D exposure in the risk of prostate cancer. Future studies are warranted to corroborate these findings and elucidate potential mechanisms underlying these associations. [ABSTRACT FROM AUTHOR]

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories. Partial least squares discriminant analysis (PLSDA) identifies distinct spectral variations in the fingerprint region, reflecting fundamental biochemical differences between lymphoma, nonmalignant lymphoid tissue, and their subtypes. These spectral features offer valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories. [ABSTRACT FROM AUTHOR]

Purpose: This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. Methods: This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. Results: The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Conclusion: Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI. [ABSTRACT FROM AUTHOR]

Introduction: Urine-derived exosomes could potentially be biomarkers for bladder cancer (BC) diagnosis. This study aimed to systematically evaluate the diagnostic worth of urine-derived exosomes in BC patients through a meta-analysis of diverse studies. Methods: A systematic search was carried out in PubMed, Web of Science, Embase, Cochrane, and CNKI databases to obtain the literature concerning the diagnosis of BC via urine-derived exosomes. A literature retrieval strategy was devised to pick articles and extract needed data from the literature. QUADS-2 was used to evaluate the quality of the included literatures, and the aggregated diagnostic effect was assessed by calculating the area under the aggregated SROC curve. All statistical analyses and plots were conducted with STATA 14.0 and RevMan5.3. Results: A total of 678 articles were retrieved by means of the search strategy of the online database. Through screening, 21 articles were obtained, involving 3348 participants and 77 studies. The meta-analysis of the results indicated that urinary exosomes had a combined sensitivity of 0.75, a specificity of 0.77, and a combined AUC of 0.83 for the diagnosis of BC, suggesting that urine-derived exosomes have a relatively satisfactory diagnostic effect in the detection of BC. Among the subgroups classified by biomarker, long non-coding RNAs (lncRNAs) had the highest comprehensive sensitivity (SEN = 0.78), and miRNAs had the highest comprehensive specificity (SPN = 0.81). In other subgroup analyses, the biomarker panel for multiple exosomes combined diagnosis demonstrated the best diagnostic efficacy, with a combined the area under the curve (AUC) of 0.87. Conclusions: As a novel biomarker, urine-derived exosomes have significant diagnostic prospects in the diagnosis of BC. Nevertheless, their application in clinical settings still demands a considerable number of clinical trials to confirm their clinical feasibility and practicability. [ABSTRACT FROM AUTHOR]

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95. [ABSTRACT FROM AUTHOR]