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Background: Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody–drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable. Methods: We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions. Results: Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes. Conclusion: Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology. [ABSTRACT FROM AUTHOR]

Background: "Batch effect" in MR images, due to vendor‐specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability. Purpose: We aim to develop a DL model using contrast adjustment and super‐resolution to reduce diffusion‐weighted images (DWIs) diversity across magnetic field strengths and imaging parameters. Study Type: Retrospective. Subjects: The DL model was built using an open dataset from one individual. The MR machine identification model was trained and validated on a dataset of 1134 adults (54% females, 46% males), with 1050 subjects showing no DWI abnormalities and 84 with conditions like stroke and tumors. The 21,000 images were divided into 80% for training, 20% for validation, and 3500 for testing. Field Strength/Sequence: Seven MR scanners from four manufacturers with 1.5 T and 3 T magnetic field strengths. DWIs were acquired using spin‐echo sequences and high‐resolution T2WIs using the T2‐SPACE sequence. Assessment: An experienced, board‐certified radiologist evaluated the effectiveness of restoring high‐resolution T2WI and harmonizing diverse DWI with metrics such as PSNR and SSIM, and the texture and frequency attributes were further analyzed using gray‐level co‐occurrence matrix and 1‐dimensional power spectral density. The model's impact on machine‐specific characteristics was gauged through the performance metrics of a ResNet‐50 model. Comprehensive statistical tests were employed for statistical robustness, including McNemar's test and the Dice index. Results: Our DL protocol reduced DWI contrast and resolution variation. ResNet‐50 model's accuracy decreased from 0.9443 to 0.5786, precision from 0.9442 to 0.6494, recall from 0.9443 to 0.5786, and F1 score from 0.9438 to 0.5587. The t‐SNE visualization indicated more consistent image features across multiple MR devices. Autoencoder halved learning iterations; Dice coefficient >0.74 confirmed signal reproducibility in 84 lesions. Conclusion: This study presents a DL strategy to mitigate batch effects in diffusion MR images, improving their quality and generalizability. Evidence Level: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Aging is a multifaceted process influenced by hereditary factors, lifestyle, and environmental elements. As time progresses, the human body experiences degenerative changes in major functions. The external and internal signs of aging manifest in various ways, including skin dryness, wrinkles, musculoskeletal disorders, cardiovascular diseases, diabetes, neurodegenerative disorders, and cancer. Additionally, cancer, like aging, is a complex disease that arises from the accumulation of various genetic and epigenetic alterations. Circadian clock dysregulation has recently been identified as an important risk factor for aging and cancer development. Natural compounds and herbal medicines have gained significant attention for their potential in preventing age-related diseases and inhibiting cancer progression. These compounds demonstrate antioxidant, anti-inflammatory, anti-proliferative, pro-apoptotic, anti-metastatic, and anti-angiogenic effects as well as circadian clock regulation. This review explores age-related diseases, cancers, and the potential of specific natural compounds in targeting the key features of these conditions. [ABSTRACT FROM AUTHOR]

Background: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying Tstage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods: We have developed a transformer-basedmulti-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrastenhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy. [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images. Method: This paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region. Results: The performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score. Discussion: Our results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm. [ABSTRACT FROM AUTHOR]

Background: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. Methods: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. Results: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. Conclusions: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours. [ABSTRACT FROM AUTHOR]

Epstein–Barr virus–associated gastric cancer (EBVaGC) may be a meaningful biomarker for potential benefit from immunotherapy. Further investigation is needed to characterize the immune landscape of EBVaGC. We assessed our institutional frequency of surgically treated EBVaGC and analyzed the immunologic biomarker profile and tumor-infiltrating lymphocyte (TIL) phenotypes of a series of EBVaGC compared to non-EBVaGC cases. Available tissue samples from all patients with biopsy-confirmed gastric adenocarcinoma who underwent resection with curative intent from 2012 to 2020 at our institution were collected. In situ hybridization was used to assess EBV status; multiplex immunohistochemistry was performed to assess mismatch repair status, Programmed Death-Ligand 1 (PD-L1) expression, and phenotypic characterization of TILs. Sixty-eight samples were included in this study. EBVaGC was present in 3/68 (4%) patients. Among all patients, 27/68 (40%) had positive PD-L1 expression; two of three (67%) EBVaGC patients exhibited positive PD-L1 expression. Compared to non-EBVaGC, EBV-positive tumors showed 5-fold to 10-fold higher density of TILs in both tumor and stroma and substantially elevated CD8+ T cell to Tregulatory cell ratio. The memory subtypes of CD8+ and CD4+ T cells were upregulated in EBVaGC tumors and stromal tissue compared to non-EBVaGC. The incidence of surgically resected EBVaGC at our center was 4%. EBVaGC tumors harbor elevated levels of TILs, including memory subtypes, within both tumor and tumor-related stroma. Robust TIL presence and upregulated PD-L1 positivity in EBVaGC may portend promising responses to immunotherapy agents. Further investigation into routine EBV testing and TIL phenotype of patients with gastric cancer to predict response to immunotherapy may be warranted. [ABSTRACT FROM AUTHOR]

Background The coronavirus-19 pandemic has impacted the delivery of medical education in dermatology, leading to decreased patient contact. There arose a need to pioneer innovative teaching tools to augment current methods for now and beyond the pandemic. Objectives We aimed to assess the utility of three-dimensional (3D) images in the learning and teaching of dermatology by analysing the perceptions of medical undergraduates and faculty members in a qualitative and quantitative study. Methods Medical undergraduates (n=119) and dermatology faculty members (n=20) were recruited on a voluntary basis to watch a showcase session using a portable 3D imaging system allowing 3D images of skin lesions to be examined and digitally manipulated. After the session, participants filled in an anonymous questionnaire evaluating their perceptions. Results Of the 119 learners, most (> 84%) strongly agreed/agreed that (i) they would have more confidence in the field of dermatology; (ii) their ability to describe skin lesions would increase; (iii) their understanding of common dermatological conditions would increase; (iv) 3D images allow a greater approximation to real-life encounters than 2D images; and (v) learning with this modality would be useful. Of the 20 faculty members, most (> 84%) strongly agreed/agreed that (i) it is easier to teach with the aid of 3D images, and (ii) they would want access to 3D images during teaching sessions. Skin tumours were perceived to be learnt best via this modality in terms of showcasing topography (P<0.01) and close approximation to real-life (P<0.001). Overall, thematic analysis from qualitative analysis revealed that conditions learnt better with 3D images were those with surface changes and characteristic topography. Conclusions Our results show that the greatest utility of 3D images lies in conditions where lesions have skin surface changes in the form of protrusions or depressions, such as in skin tumours or ulcers. As such, 3D images can be useful teaching tools in dermatology, especially in conditions where appreciation of surface changes and topography is important. [ABSTRACT FROM AUTHOR]

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC). In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers—linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)—were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic–radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs). Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set. ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy. [ABSTRACT FROM AUTHOR]

The limited reproducibility of the grading of non-muscle invasive papillary urothelial carcinoma (NMIPUC) necessitates the search for more robust image-based predictive factors. In a cohort of 157 NMIPUC patients treated with Bacille Calmette–Guérin (BCG) immunotherapy, we explored the multiple instance learning (MIL)-based classification approach for the prediction of 2-year and 5-year relapse-free survival and the multiple instance survival learning (MISL) framework for survival regression. We used features extracted from image patches sampled from whole slide images of hematoxylin–eosin-stained transurethral resection (TUR) NPMIPUC specimens and tested several patch sampling and feature extraction network variations to optimize the model performance. We selected the model showing the best patient survival stratification for further testing in the context of clinical and pathological variables. MISL with the multiresolution patch sampling technique achieved the best patient risk stratification (concordance index = 0.574, p = 0.010), followed by a 2-year MIL classification. The best-selected model revealed an independent prognostic value in the context of other clinical and pathologic variables (tumor stage, grade, and presence of tumor on the repeated TUR) with statistically significant patient risk stratification. Our findings suggest that MISL-based predictions can improve NMIPUC patient risk stratification, while validation studies are needed to test the generalizability of our models. [ABSTRACT FROM AUTHOR]

Background: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. Methods: This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. Results: We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18–5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07–4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. Conclusion: Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume measured on computed tomography (CT) images and the life expectancy of patients with oral cancer. We also developed a learning model using deep learning to automatically extract the masseter muscle volume and investigated its association with the life expectancy of oral cancer patients. Methods: To develop the learning model for masseter muscle volume, we used manually extracted data from CT images of 277 patients. We established the association between manually extracted masseter muscle volume and the life expectancy of oral cancer patients. Additionally, we compared the correlation between the groups of manual and automatic extraction in the masseter muscle volume learning model. Results: Our findings revealed a significant association between manually extracted masseter muscle volume on CT images and the life expectancy of patients with oral cancer. Notably, the manual and automatic extraction groups in the masseter muscle volume learning model showed a high correlation. Furthermore, the masseter muscle volume automatically extracted using the developed learning model exhibited a strong association with life expectancy. Conclusions: The sarcopenia assessment method is useful for predicting the life expectancy of patients with oral cancer. In the future, it is crucial to validate and analyze various factors within the oral surgery field, extending beyond cancer patients. [ABSTRACT FROM AUTHOR]

Background: PD-L1/PD-1 molecules are known as important mediators in immune-escape mechanisms of tumors. PD-L1 is highly expressed in various malignancies, including bladder cancer. However, the prognostic value of PD-L1 in bladder cancer patients remains controversial. Aim: To investigate the prognostic significance of PD-L1 expression in tumor tissues of bladder cancer patients. Subjects and Methods: RNA was isolated from FFPE tumor tissues of 48 bladder cancer patients using the monophasic phenol and guanidine isothiocyanate method. Total RNA was converted to cDNA and gene expression levels were analyzed by qRT-PCR. The differential expression levels of the PD-L1 gene between tumor grade and cancer stage groups were analyzed by independent student's t-test and one-way ANOVA. Results: Statistically significantly increased PD-L1 expression was observed in the high-grade tumor group (p < 0.05). No significant difference in PD-L1 expression was found among pTa, pT1, and pT2 groups. In addition, the difference in overall survival was not significantly different between groups. Conclusion: The results showed that high PD-L1 expression in bladder cancer was associated with tumor aggressiveness and grade. Despite the inability of the qRT-PCR to show the PD-L1 expression at different locations of tumor tissue, evaluation of PD-L1 mRNA expression by qRT-PCR, which is a highly sensitive and specific assay, appears to be a robust approach. Furthermore, these findings may contribute to a rationale for recommending anti-PD-L1 immunotherapy as an alternative to standard therapy for bladder cancer patients who are most likely to benefit from it. [ABSTRACT FROM AUTHOR]

Introduction: Colonoscopy is the gold standard for early diagnosis of colorectal cancer. Screening programs are recommended for individuals between the ages of 45-75. In this study, we aimed to evaluate the relationship between indications for colonoscopy and malignancy in patients aged 75 and above. Materials and Methods: Between 2021 and 2023, 12,416 colonoscopic procedures performed in our endoscopy unit were retrospectively analyzed. Of these, 946 were patients aged 75 and over. After excluding those with inaccessible data, incomplete colonoscopies, or a history of colorectal cancer/polyps, 398 were included in the study. Symptoms were categorized as follows: macroscopic bleeding, anemia, changes in bowel habits, suspicion of malignancy, weight loss, and non-specific symptoms. Rates of malignancy were subsequently determined. Results: The median age of all patients was 77 years (minimum: 75, maximum: 97), with 51.3% being male. Macroscopic bleeding was the most common symptom at 29.9%, followed by changes in bowel habits (28.6%), anemia (25.1%), non-specific symptoms (7.8%), suspicion of malignancy (6.5%), and weight loss (3.3%). Malignancy was detected in 10.8% of patients, with 55.8% of those diagnosed being female. The most prevalent symptom among these patients was macroscopic bleeding (44.2%), then anemia (25.6%), bowel habit changes (2%), suspicion of malignancy (14%), non-specific symptoms (4.7%), and weight loss (2.3%). Macroscopic bleeding (p=0.030) and suspicion of malignancy (p=0.037) were statistically significant in predicting malignancy, whereas the other symptoms were not. Conclusion: Colonoscopy can be safely performed in patients aged 75 and over. It is particularly critical for patients presenting with macroscopic bleeding and suspicion of malignancy. However, the utility of colonoscopy for other symptoms warrants further evaluation. [ABSTRACT FROM AUTHOR]

Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tumor segmentation in colorectal ultrasound images was developed, to provide real-time guidance on resection margins using intra-operative ultrasound. A colorectal ultrasound dataset was acquired consisting of 179 images from 74 patients, with ground truth tumor annotations based on histopathology results. To address data scarcity, transfer learning techniques were used to optimize models pre-trained on breast ultrasound data for colorectal ultrasound data. A new custom gradient-based loss function (GWDice) was developed, which emphasizes the clinically relevant top margin of the tumor while training the networks. Lastly, ensemble learning methods were applied to combine tumor segmentation predictions of multiple individual models and further improve the overall tumor segmentation performance. Transfer learning outperformed training from scratch, with an average Dice coefficient over all individual networks of 0.78 compared to 0.68. The new GWDice loss function clearly decreased the average tumor margin prediction error from 1.08 mm to 0.92 mm, without compromising the segmentation of the overall tumor contour. Ensemble learning further improved the Dice coefficient to 0.84 and the tumor margin prediction error to 0.67 mm. Using transfer and ensemble learning strategies, good tumor segmentation performance was achieved despite the relatively small dataset. The developed US segmentation model may contribute to more accurate colorectal tumor resections by providing real-time intra-operative feedback on tumor margins. [ABSTRACT FROM AUTHOR]

Purpose: Tumor boards serve as established platforms for interdisciplinary expert discussions and therapeutic recommendations tailored to individual patient characteristics. Despite their significance, medical students often lack exposure to such interdisciplinary discussions as tumor boards are currently not integrated into medical curricula. To address this, we aimed to enhance future physicians' interdisciplinary communication skills and subject-specific knowledge by introducing an interactive series of five linked tumor board seminars within the domain of neuro-oncology. Methods: We developed a neuro-oncological student tumor board using a flipped-classroom format. The primary objectives of this case-centered approach included fostering an understanding of the tumor board process, active participation in multidisciplinary case discussions, honing appropriate communication strategies, and creating personalized therapy plans that consider inputs from all relevant disciplines, individual patient factors, and ethical considerations. To gauge the effectiveness of the seminar series, we administered structured pre- and post-course questionnaires. Results: Fourteen medical students in third to fifth year participated in the pilot series. Despite its organizational complexity, the interdisciplinary seminars were feasible. Students demonstrated significant growth in competence, aligned with predefined learning objectives. Notably, they appreciated the supportive learning environment and interactive teaching format, which kindled their interest in interdisciplinary oncology. Conclusion: Active participation in a student tumor board can empower students to tackle the diverse challenges of caring for cancer patients within an interdisciplinary team during the early stages of their careers. The student tumor board represents an innovative, learner-centered approach to teach interdisciplinary cancer treatment, communication strategies, and ethical aspects of medical practice. [ABSTRACT FROM AUTHOR]

Pleomorphic dermal sarcoma (PDS)is a rare dermal-based tumor, with low-grade malignant behavior. Ifsknown for its rapid growth, exophytic nature and tendency to bleed. It commonly occurs in elderly individuals on sun-exposed skin regions and shares similarities with atypical fibroxanthoma (AFX). We present a case of a 55-year-old male patient exhibiting an exophytic lesion on the back of his neck while undergoing immunotherapy because of metastatic Clear cell renal cell carcinoma (ccmRCC). [ABSTRACT FROM AUTHOR]

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that the incidence of colorectal cancer among younger persons is rising. We investigated incidence trends and survival in the German federal state of North Rhine-Westphalia. Methods: Cancer registry data from the period 2008-2019 were classified into two age groups (15-54 and 55-99). In each age group, the standardized incidence, average annual percent change (AAPC), and relative 5-year survival (RS) were calculated and stratified according to the site, histology, size, and grade of the colorectal tumor. Results: 167 919 cases were included, with adenocarcinoma accounting for 86.4%. In 2019, the age-standardized incidence per 100 000 person-years was 13.8 and 10.3 among men and women (respectively) in the younger age group, compared with 197.9 and 126.3 in the older age group. Over the study period from 2008 to 2019, the incidence declined among older men and women (AAPC -2.6% and -2.9%) but remained nearly constant among younger men and women (-0.5% and -0.4%). The incidence of neuroendocrine, T1, and G2 tumors rose in both age groups (AAPC range: 2.3%-12.2%; 2.2%-8.3%, 6.3%-8.8%). Younger patients have a better RS, with the largest difference between age groups being found for neuroendocrine tumors (88% and 83% in younger men and women, 65% and 61% in older men and women). Conclusion: The incidence of colorectal tumors has remained constant in persons under age 55 and declined in persons aged 55 and older. Nonetheless, the incidence of neuroendocrine tumors and of small and well-differentiated tumors has risen in both age groups. The trends among younger persons and the rise in neuroendocrine tumors merit further study. [ABSTRACT FROM AUTHOR]

Although brown fat is strongly associated with a constellation of cardiometabolic benefits in animal models and humans, it has also been tied to cancer cachexia. In humans, cancer-associated cachexia increases mortality, raising the possibility that brown fat in this context may be associated with increased cancer death. However, the effect of brown fat on cancer-associated cachexia and survival in humans remains unclear. Here, we retrospectively identify patients with and without brown fat on fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) scans obtained as part of routine cancer care and assemble a cohort to address these questions. We did not find an association between brown fat status and cachexia. Furthermore, we did not observe an association between brown fat and increased mortality in patients with cachexia. Our analyses controlled for confounding factors including age at cancer diagnosis, sex, body mass index, cancer site, cancer stage, outdoor temperature, comorbid conditions (heart failure, type 2 diabetes mellitus, coronary artery disease, hypertension, dyslipidemia, cerebrovascular disease), and β-blocker use. Taken together, our results suggest that brown fat is not linked to cancer-associated cachexia and does not worsen overall survival in patients with cachexia. [ABSTRACT FROM AUTHOR]

Background: Adolescents and young adults (AYA, ages 15–39 years) affected by cancer have unique treatment, survivorship, and palliation concerns. Current oncology training does not focus on the distinctive needs of this demographic. Amid this recognition, the Canadian National AYA Cancer Task Force and Canadian Partnership Against Cancer have advocated the need for clinicians with formalized AYA experience. To address this need and standardize training, a national task force developed criteria for structured academic programs in AYA Oncology in Canada. Methods: Workshops were organized to identify and establish the fundamentals of practice in AYA Oncology through consensus. These workshops followed the pre‐existing rigorous process established by the Royal College of Physicians and Surgeons of Canada (Royal College) for new program development. The process includes: (i) developing the tasks associated with the discipline's practice, (ii) identifying the evidence trainees must provide to demonstrate tasks can be performed independently (the competence portfolio), (iii) developing training requirements and summarizing the knowledge, skills and attitudes required to perform these tasks, and (iv) identifying specific experiences essential to acquiring skills and demonstrating competent performance. Results: AYA Oncology is a recognized an Area of Focused Competence (AFC) by the Royal College. Conclusion: The AFC designation in AYA Oncology provides a standardized curriculum, training experience and accreditation process to attract oncologists, promote expertise and advance AYA oncology care. [ABSTRACT FROM AUTHOR]

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Background: The impact of cancer extends beyond patients and consumes their families. Family members are widely recognized as informal caregivers. The economic burden on family caregivers is increased with new treatments, prolonged survival, and reduced stay in the acute care setting. This is especially true in African countries where family bonds are sacred and health system is fragile that they need to pay out of pocket for care. The aim of this study is to estimate the perceived caregivers' economic burden in the subsequent aspects: financial strain, inability to make ends meet, not enough money for necessities, and economic adjustments/cutbacks. Method: This study was a quantitative, descriptive cross-sectional study conducted at Khartoum oncology hospital. Included 143 caregivers of cancer patients. Data were collected through face-to-face interviews using the socio-demographic Questionnaire and Economic Hardship Questionnaire (EHQ). Results: One hundred forty-three cancer patients and their caregivers were included. 56.6% of patients were females, and about 32.2% were aged 51–65 years. The most common cancer types were breast cancer and leukemia. Roughly 33% of patients had stage IV cancer on presentation, and about 53.9% received chemotherapy. Unlike cancer patients, (47.6%) of family caregivers were aged 18–34 years, yet they were mainly females (54.4%). Most of them (34.3%) were unemployed, with a mean monthly gross income of 53.3 dollars, while the mean household monthly gross income was 113.0 dollars. The mean score of the economic hardship scale was 35.8 out of 64. Most of the caregivers experience no difficulties affording necessities. However, they experience difficulties with medical and leisure activities. There was no significant association between caregiver economic hardship and cancer patient characteristics (patients' age, cancer stage, and treatment type). However, there was a significant association between caregivers' economic hardships and their gender, marital status, educational level, occupation, caregiver monthly gross income, and household monthly gross income. Conclusion: The study findings suggest a moderate financial burden among cancer caregivers. The predicting factors include being single, a student, male, of higher educational level, and lower income. Financial difficulties are associated with maladaptive behavior and should come to light. [ABSTRACT FROM AUTHOR]

Aging and cancer are clearly associated processes, at both the epidemiological and molecular level. Epigenetic mechanisms are good candidates to explain the molecular links between the two phenomena, but recent reports have also revealed considerable differences, particularly regarding the loss of DNA methylation in the two processes. The large‐scale generation and availability of genome‐wide epigenetic data now permits systematic studies to be undertaken which may help clarify the similarities and differences between aging and cancer epigenetic alterations. In addition, the development of epigenetic clocks provides a new dimension in which to investigate diseases at the molecular level. Here, we examine current and future questions about the roles of DNA methylation mechanisms as causal factors in the processes of aging and cancer so that we may better understand if and how aging‐associated epigenetic alterations lead to tumorigenesis. It seems certain that comprehending the molecular mechanisms underlying epigenetic clocks, especially with regard to somatic stem cell aging, combined with applying single‐cell epigenetic‐age profiling technologies to aging and cancer cohorts, and the integration of existing and upcoming epigenetic evidence within the genetic damage models of aging will prove to be crucial to improving understanding of these two interrelated phenomena. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Medicina de la Universidad Nacional de Colombia is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forkhead box O (FOXO) proteins are transcription factors involved in cancer and aging and their pharmacological manipulation could be beneficial for the treatment of cancer and healthy aging. FOXO proteins are mainly regulated by post-translational modifications including phosphorylation, acetylation and ubiquitination. As these modifications are reversible, activation and inactivation of FOXO factors is attainable through pharmacological treatment. One major regulatory input of FOXO signaling is mediated by protein kinases. Here, we use specific inhibitors against different kinases including PI3K, mTOR, MEK and ALK, and other receptor tyrosine kinases (RTKs) to determine their effect on FOXO3 activity. While we show that inhibition of PI3K efficiently drives FOXO3 into the cell nucleus, the dual PI3K/mTOR inhibitors dactolisib and PI-103 induce nuclear FOXO translocation more potently than the PI3Kδ inhibitor idelalisib. Furthermore, specific inhibition of mTOR kinase activity affecting both mTORC1 and mTORC2 potently induced nuclear translocation of FOXO3, while rapamycin, which specifically inhibits the mTORC1, failed to affect FOXO3. Interestingly, inhibition of the MAPK pathway had no effect on the localization of FOXO3 and upstream RTK inhibition only weakly induced nuclear FOXO3. We also measured the effect of the test compounds on the phosphorylation status of AKT, FOXO3 and ERK, on FOXO-dependent transcriptional activity and on the subcellular localization of other FOXO isoforms. We conclude that mTORC2 is the most important second layer kinase negatively regulating FOXO activity. [ABSTRACT FROM AUTHOR]

Background: An estimated 400,000 children develop cancer worldwide. Of those, 90% occur in low- and middle-income countries, where survival rates can be as low as 30%. To reduce the childhood cancer survival gap between high- and low- and middle-income countries (LMIC), the World Health Organization launched the Global Initiative for Childhood Cancer in 2018, to support governments in building sustainable childhood cancer programs, with the aim to increase access and quality of care for children with cancer. Developing a high-quality and trained workforce is key to the success of childhood cancer services, but more information is needed on the interventions used to develop and train a workforce. The objective of this review is to understand the key factors described in the literature in relation to the development and training of a workforce in childhood cancer (defined here as ages 0–19) in LMIC, including challenges, interventions and their outcomes. Methods: We will include sources of evidence that describe the development or training of a childhood cancer workforce in health services that diagnose, refer or treat children and adolescents with cancer, in low- and middle-income countries as defined by the World Bank. The following databases will be searched: OVID Medline, Embase and Pubmed from 2001 to present with no restriction of language. Grey literature searches will also be performed in Proquest Dissertation and Theses, as well as relevant organizations' websites, and conference proceedings will be searched in conference websites. In addition, references lists will be reviewed manually. Two people will screen abstracts and full-texts and extract data. Data will be presented in a table or chart, with an accompanying narrative summary responding to the review questions. A framework synthesis will be conducted: data will be charted against a framework adapted from the 2016 WHO Global Strategy for Human Resources for Heath: Workforce 2030. Discussion: This scoping review will allow to map the existing literature on workforce development in LMIC, identify potential interventions and highlight data and knowledge gaps. This constitutes a first step towards adopting successful strategies more broadly, formulating research priorities and developing effective policies and interventions. Systematic review registration: Open Science Framework osf.io/3mp7n [ABSTRACT FROM AUTHOR]

Recently, there has been an increase in the incidence of malignant tumors among the older population. Moreover, there is an association between aging and cancer. During the process of senescence, the human body suffers from a series of imbalances, which have been shown to further accelerate aging, trigger tumorigenesis, and facilitate cancer progression. Therefore, exploring the junctions of aging and cancer and searching for novel methods to restore the junctions is of great importance to intervene against aging-related cancers. In this review, we have identified the underlying pathogenetic mechanisms of aging-related cancers by comparing alterations in the human body caused by aging and the factors that trigger cancers. We found that the common mechanisms of aging and cancer include cellular senescence, alterations in proteostasis, microbiota disorders (decreased probiotics and increased pernicious bacteria), persistent chronic inflammation, extensive immunosenescence, inordinate energy metabolism, altered material metabolism, endocrine disorders, altered genetic expression, and epigenetic modification. Furthermore, we have proposed that aging and cancer have common means of intervention, including novel uses of common medicine (metformin, resveratrol, and rapamycin), dietary restriction, and artificial microbiota intervention or selectively replenishing scarce metabolites. In addition, we have summarized the research progress of each intervention and revealed their bidirectional effects on cancer progression to compare their reliability and feasibility. Therefore, the study findings provide vital information for advanced research studies on age-related cancers. However, there is a need for further optimization of the described methods and more suitable methods for complicated clinical practices. In conclusion, targeting aging may have potential therapeutic effects on aging-related cancers. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is the fourth leading cause of cancer mortality representing 7.5% of all female cancer deaths. In India, it is the second most frequent cancer among women and it usually occurs between 15 and 44 years of age. The key to prevent this cancer is early detection. Hence, awareness regarding cervical cancer and screening measures for cervical cancer should be spread widely in the community. This study was taken up to evaluate the knowledge of the symptoms, risk factors, and preventive measures for cervical cancer among undergraduate female physiotherapy students. Methods: This observational study was carried out over a period of 3 months among 154 female undergraduate physiotherapy students in Belagavi city, Karnataka, India. A pretested self-administered questionnaire was provided to evaluate their basic knowledge and awareness about cervical cancer. Statistical analysis was done using SPSS version. 23. Results: Knowledge regarding having multiple sexual partners as one of the risk factors for cervical cancer was the commonest among the undergraduate students (76.6%) and least for early menarche (24%). HPV vaccination as a preventive measure was known by only 20% of students. Only 9.7% knew the appropriate age to get vaccinated. Conclusion: The present study demonstrated that the undergraduate students had poor knowledge and awareness regarding risk factors for cervical cancer, human papillomavirus and its vaccination. [ABSTRACT FROM AUTHOR]

TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages. [ABSTRACT FROM AUTHOR]

Copyright of Hamidiye Medical Journal is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms. Methods: A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals. Results: Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals. Conclusions: T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients. [ABSTRACT FROM AUTHOR]

Background: Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia. Methods: This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization. Results: The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors. Conclusions: This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively. [ABSTRACT FROM AUTHOR]

The spread of early‐stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end‐to‐end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population‐based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67–0.758) and patients with any LNM with an AUROC of 0.711 (0.597–0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644–0.778) and 0.567 (0.542–0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Introduction: The cooperative group experience of thoracic sterotactic body radiation therapy (SBRT) in medically inoperable patients with early stage non-small cell lung cancer (NSCLC) historically utilized corticosteroid premedication. Patterns of care have been mixed as to whether premedication adds benefit in terms of improved lung toxicity and treatment tolerance. Methods: Patients treated for NSCLC from 2014 to 2017 with definitive thoracic SBRT (BED10≥100) at a single institution, in a prospectively collected database were evaluated. Pretreatment clinicopathologic characteristics, including Eastern Cooperative Oncology Group (ECOG) performance status, PFT parameters of FEV1, and diffusing capacity for carbon monoxide (DLCO) were collected. Treatment and dosimetric characteristics were collected, and patients were scored as to whether dexamethasone was prescribed and utilized with each fraction. Toxicity was graded on multiple domains including lung as during and 30 days after completion of treatment using Common Terminology Criteria for Adverse Events Version 4. Univariate analysis was performed with Fisher's exact test for categorical variables and two-tailed Student's t -test for continuous variables. Multivariate analysis was performed with Cox proportional hazards model to adjust for age, pretreatment DLCO, ECOG, tumor size, central versus peripheral location, and biological effective dose. Results: A total of 86 patients treated with thoracic SBRT with 54–60 Gy in 3–8 fractions met inclusion criteria, with the majority (70%) receiving 5 fractions. Of these patients, 45 (52%) received 4 mg dexamethasone premedication prior to each fraction of SBRT and 41 (48%) were treated without dexamethasone premedication. Overall acute lung toxicity was low in both groups. Between the two groups of patients, 5/45 (11%) developed grade 2 or higher lung toxicity including hospital admission in the dexamethasone premedication arm vs. 2/41 (5%) without premedication (p = 0.4370, Fisher's exact test). Freedom from acute SBRT lung toxicity was no different between dexamethasone premedication arm and no premedication (Log rank, p = 0.45). On multivariate Cox proportional hazard modeling adjusting for age, ECOG, tumor size, central vs. peripheral location, pretreatment DLCO, and BED, there was no difference in freedom from acute lung toxicity without dexamethasone premedication (HR: 0.305; 95% CI: 0.033, 2.792; p = 0.293). Conclusions: In this retrospective analysis, pretreatment steroid prophylaxis with dexamethasone confers a similar acute toxicity profile and no added clinical benefit to treatment without pretreatment steroid prophylaxis. [ABSTRACT FROM AUTHOR]

This article presents data on the age structure of robot-assisted surgery operated on in patients with gynecological oncological diseases in Bulgaria over a two-year period. Aim: to determine the incidence of oncological diseases in gynecology by age groups and to calculate the growth rate of morbidity by age, in order to extrapolate for evaluation for a future period. Method: Statistics on robot-assisted robot-assisted surgery in gynecology from a two-year period in oncological diseases were used. Results: out of a total of three hundred and eight cases studied, the largest number of cases of oncological diseases in gynecology were registered in the age group from fifty-two years to sixty-one years, followed by the age group from sixty-two years to seventy-one years and the age group from forty-two to fifty-one years. The decline in morbidity over the age of eighty is explained by natural demographic reasons. Discussion: between the ages of forty-two and seventy-two, two physiological periods overlap with a predominance of cancer, respectively - hormonal changes and geriatric changes. Probable causes are delayed metabolism and decreased immune function, increasing age and the influence of external environmental factors such as radiation. The latter may be a lifelong factor, but the weakening of the immune system is a breakthrough in its impact. Conclusion: the age structure of tumors in gynecology marks an uneven distribution of cases by age, peaking around the sixties, based on the influence of internal factors transition from reproductive to geriatric age. [ABSTRACT FROM AUTHOR]

This article discusses the use of a symbol-based mnemonic called the "tumour triangle" to teach important clinical characteristics of skin cancer. The mnemonic consists of three corners representing morphology and lesion site, growth dynamics and change, and patient profile and risk factors. By using this mnemonic, teachers can enhance students' visual recognition skills and improve their ability to distinguish between benign and malignant tumors based on clinical information. The article emphasizes the importance of repeated practice and immediate feedback in developing these skills. The use of symbols can also facilitate discussions and communication among students. [Extracted from the article]

Objectives: This study aims to evaluate the value of machine learning-based dynamic contrast-enhanced MRI (DCE-MRI) radiomics nomogram in prediction treatment response of neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. Methods: A total of 102 patients with osteosarcoma and who underwent NAC were enrolled in this study. All patients received a DCE-MRI scan before NAC. The Response Evaluation Criteria in Solid Tumors was used as the standard to evaluate the NAC response with complete remission and partial remission in the effective group, stable disease, and progressive disease in the ineffective group. The following semi-quantitative parameters of DCE-MRI were calculated: early dynamic enhancement wash-in slope (Slope), time to peak (TTP), and enhancement rate (R). The acquired data is randomly divided into 70% for training and 30% for testing. Variance threshold, univariate feature selection, and least absolute shrinkage and selection operator were used to select the optimal features. Three classifiers (K-nearest neighbor, KNN; support vector machine, SVM; and logistic regression, LR) were implemented for model establishment. The performance of different classifiers and conventional semi-quantitative parameters was evaluated by confusion matrix and receiver operating characteristic curves. Furthermore, clinically relevant risk factors including age, tumor size and site, pathological fracture, and surgical staging were collected to evaluate their predictive values for the efficacy of NAC. The selected clinical features and imaging features were combined to establish the model and the nomogram, and then the predictive efficacy was evaluated. Results: The clinical relevance risk factor analysis demonstrates that only surgical stage was an independent predictor of NAC. A total of seven radiomic features were selected, and three machine learning models (KNN, SVM, and LR) were established based on such features. The prediction accuracy (ACC) of these three models was 0.89, 0.84, and 0.84, respectively. The area under the subject curve (AUC) of these three models was 0.86, 0.92, and 0.93, respectively. As for Slope, TTP, and R parameters, the prediction ACC was 0.91, 0.89, and 0.81, respectively, while the AUC was 0.87, 0.85, and 0.83, respectively. In both the training and testing sets, the ACC and AUC of the combined model were higher than those of the radiomics models (ACC = 0.91 and AUC = 0.95), which indicate an outstanding performance of our proposed model. Conclusions: The radiomics nomogram demonstrates satisfactory predictive results for the treatment response of patients with osteosarcoma before NAC. This finding may provide a new decision basis to improve the treatment plan. [ABSTRACT FROM AUTHOR]

Objective: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors. Patients and Methods: Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status. Results: With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678–0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05–62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77–56.41%) and 69.65% (95% CI 68.21–71.1%), respectively. These results were confirmed via cross‐validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02–1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96–35.7; P < 0.001) proved to be independent predictors for LNM. Conclusion: In our present study, CNN‐based image analyses showed promising results as a potential novel low‐cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction. [ABSTRACT FROM AUTHOR]

Osteochondral destruction and a high recurrence rate after surgery are major concerns that make difficult the treatment course of tenosynovial giant cell tumor. The aims of this study were to elucidate rates of postoperative local recurrence and osteochondral destruction, as correlated with various demographic factors. Eighty surgically treated patients with intra-articular tumors (knee: 49, ankle and foot: 12, hip: 10, others: 9) were included in this study. Factors including age, disease type (diffuse/localized), location, existence of osteochondral destruction were correlated with local recurrence or development/progression of osteochondral destruction. The 5-year local recurrence free survival rate was 71.4%. Diffuse type (n = 59, localized: n = 21) (P = 0.023) and knee location (P = 0.002) were independent risk factors for local recurrence. Diffuse type (P = 0.009) was a significant risk factor, and knee location (P = 0.001) was a negative factor for osteochondral destruction at the initial examination. Progression of osteochondral destruction was observed more often in cases with local recurrence (P = 0.040) and findings of osteochondral destruction at the initial examination (P = 0.029). Diffuse type is a factor that should be noted for both local recurrence and osteochondral destruction, while local recurrence occurs but osteochondral destruction is less observed in the knee. [ABSTRACT FROM AUTHOR]

Background: Pseudocirrhosis is characterized by radiological changes in the liver that resemble cirrhosis, but with more rapid onset and progression. Though reported most frequently in patients with metastatic breast cancer, little is known about its prognostic factors and impact on breast cancer outcomes. Methods: In this observational study, we reviewed abdominal CT and/or MRI scan reports of all patients with invasive breast cancer diagnosed at our center, during a ten-year period, to identify patients with pseudocirrhosis. Exclusion criteria included lack of baseline imaging, pre-existing cirrhosis, hepatitis B or C, other chronic liver diseases, or heavy alcohol use. Routine descriptive statistical measures were used. Survival distributions were estimated using Kaplan-Meier method, and Cox regression was used for multivariate analysis. Two-tailed p < 0.05 was considered significant. Results: Eighty-six patients were included – all were females, median age was 57.5 years, and 90% were Caucasian; 86% of primary tumors were hormone-receptor positive and 17% were HER2 positive. Most patients (98%) had metastatic disease with liver involvement (94%), and were heavily pre-treated – 97% with chemotherapy, 85% with hormonal therapy, and 19% with anti-HER2 agents. Median interval from breast cancer diagnosis to pseudocirrhosis was 75.4 months (IQR 35.2-115.3 months). Thirty-six percentage of patients had ≥1 signs of portal hypertension and 49% had ≥1 signs of hepatocellular failure. Pseudocirrhosis led to permanent discontinuation of chemotherapy, endocrine therapy, and all systemic therapies in 29%, 31%, and 20% patients, respectively. Median overall survival from diagnosis of pseudocirrhosis was 10.0 months (95%CI 5.2-14.8 months). On multivariate analysis, coagulopathy, hyperbilirubinemia, hypoalbuminemia, and cancer progression were independently predictive of mortality. Conclusions: In this largest series, to date, of breast cancer with pseudocirrhosis, the latter was often complicated by portal hypertension and hepatocellular failure, and markedly impacted breast cancer management. Survival was shorter for patients who developed hepatocellular failure. [ABSTRACT FROM AUTHOR]

In the last decade, the number of breast cancer cases has increased by 35% worldwide. The screening mammography clearly reduces the relative risk of breast cancer mortality in women between 50 and 59 years old, the decrease being more important for the age range 60-69 years old. The United States of America together with international specialized groups recommend mammographic screening in women between 50 and 74 years old every 1-2 years, depending on the individual risk factors and on the patient's desire. Japan's position is that of recommending mammographic screening without clinical examination of the breasts for women between the ages of 40 and 74. In the European Union, population screening by mammography in women aged 50-69 has been recognized as a priority, along with the screening for cervical and colon cancer, since 2003. Romania presents a major delay in the implementation of organized screening of the population for breast cancer compared to other EU members, although the number of cases is increasing. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is the fourth leading cause of cancer mortality representing 7.5% of all female cancer deaths. In India, it is the second most frequent cancer among women and it usually occurs between 15 and 44 years of age. The key to prevent this cancer is early detection. Hence, awareness regarding cervical cancer and screening measures for cervical cancer should be spread widely in the community. This study was taken up to evaluate the knowledge of the symptoms, risk factors, and preventive measures for cervical cancer among undergraduate female physiotherapy students. Methods: This observational study was carried out over a period of 3 months among 154 female undergraduate physiotherapy students in Belagavi city, Karnataka, India. A pretested self-administered questionnaire was provided to evaluate their basic knowledge and awareness about cervical cancer. Statistical analysis was done using SPSS version. 23. Results: Knowledge regarding having multiple sexual partners as one of the risk factors for cervical cancer was the commonest among the undergraduate students (76.6%) and least for early menarche (24%). HPV vaccination as a preventive measure was known by only 20% of students. Only 9.7% knew the appropriate age to get vaccinated. Conclusion: The present study demonstrated that the undergraduate students had poor knowledge and awareness regarding risk factors for cervical cancer, human papillomavirus and its vaccination. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the baseline MRI characteristics for predicting survival outcomes and construct survival models for risk stratification to facilitate personalized treatment and follow-up strategies in patients with MRI-defined T3 (mrT3) locally advanced rectal cancer (LARC).Methods: We retrospectively reviewed 256 mrT3 LARC patients evaluated between 2008 and 2012 in our institution, with an average follow-up period of 6.8 ± 1.2 years. The baseline MRI characteristics, clinical data, and follow-up information were evaluated. The patients were randomized into a training cohort (TC, 186 patients) and validation cohort (VC, 70 patients). The TC dataset was used to develop multivariate nomograms for disease-free survival (DFS) and overall survival (OS), while the VC dataset was used for independent validation of the models. Harrell concordance (C) indices and Hosmer-Lemeshow calibration were used to evaluate the performances of the models.Results: Baseline mrT3 substage, extramural venous invasion (EMVI) grading, mucinous adenocarcinoma, mesorectal fascia involvement, elevated pretreatment carcinoembryonic antigen level, and neoadjuvant chemoradiotherapy (NCRT) were independent predictors of DFS. T3 substage, EMVI grading, and NCRT were also independent predictors of OS. The nomograms constructed permitted the individualized prediction of 3-year and 5-year DFS and 5-year OS with high discrimination (C-index range, 0.833-0.892) and good calibration in the TC and VC.Conclusions: We have identified baseline MRI characteristics that help independently predict survival outcomes in patients with mrT3 LARC. The survival models based on these characteristics allow for the individualized pretreatment risk stratification in patients with mrT3 LARC.Key Points: • Baseline MRI characteristics can independently stratify risk and predict survival outcomes in patients with mrT3 LARC. • The nomograms built using selected baseline MRI characteristics facilitate the individualized pretreatment risk stratification and help with clinical decision-making in patients with mrT3 LARC. • MR-defined risk factors should, therefore, be carefully reported in the baseline MRI evaluation. [ABSTRACT FROM AUTHOR]

Aims: While most guidelines still recommend colorectal cancer (CRC) screening initiation at age 50 years in average-risk individuals, guideline-creating bodies are starting to lower the recommended age of initiation to 45 years to mitigate the trend of increasing CRC rates in younger populations. Using CRC-AIM, we modeled the impact of lowering the CRC screening initiation age, incorporating theoretical and reported adherence rates, for triennial multi-target stool DNA (mt-sDNA) or annual fecal immunochemical test (FIT) screening.Methods and Materials: Screening strategies were simulated for individuals without CRC at age 40 and screened from ages 50 to 75 or 45 to 75 years. Outcomes included CRC incidence, CRC mortality, and life-years gained (LYG) per 1000 individuals screened (compared with no screening). Models used theoretically perfect (100%) and previously reported (71% mt-sDNA; 43% FIT) adherence rates.Results: With perfect adherence, mt-sDNA and FIT resulted in 22.2 and 23.4 more predicted LYG, respectively, with screening initiation at age 45 versus 50 years; reported adherence resulted in 23.9 and 24.4 more LYG, respectively. With perfect adherence, screening initiation at age 45 versus 50 years resulted in 26.1 and 28.6 CRC cases, respectively, with mt-sDNA and 22.8 and 25.5 cases with FIT; with reported real-world adherence there were 28.5 and 31.2 cases, respectively, with mt-sDNA and 37.1 and 40.2 cases with FIT. Similar patterns were observed for CRC deaths. With screening initiation at age 45 and reported adherence, mt-sDNA averted 8.6 more CRC cases and 3.3 more deaths per 1000 individuals than FIT.Conclusions: Estimated CRC screening outcomes improved by lowering the initiation age from 50 to 45 years. Incorporating reported adherence rates yields greater benefits from triennial mt-sDNA versus annual FIT screening. [ABSTRACT FROM AUTHOR]