Search Results (67)

Backgroud. Endometriosis is a chronic, estrogen-dependent inflammatory disease characterized by the ectopic presence of endometrial-like tissue. Although genome-wide association studies (GWAS) have identified susceptibility variants, their tissue-specific regulatory impact remains poorly understood. Objective. To functionally characterize endometriosis-associated variants by exploring their regulatory effects as expression quantitative trait loci (eQTLs) across six physiologically relevant tissues: peripheral blood, sigmoid colon, ileum, ovary, uterus, and vagina. Methods. GWAS-identified variants were cross-referenced with tissue-specific eQTL data from the GTEx v8 database. We prioritized genes either frequently regulated by eQTLs or showing the strongest regulatory effects (based on slope values, which indicate the direction and magnitude of the effect on gene expression). Functional interpretation was performed using MSigDB Hallmark gene sets and Cancer Hallmarks gene collections. Results. A tissue specificity was observed in the regulatory profiles of eQTL-associated genes. In the colon, ileum, and peripheral blood, immune and epithelial signaling genes predominated. In contrast, reproductive tissues showed the enrichment of genes involved in hormonal response, tissue remodeling, and adhesion. Key regulators such as MICB, CLDN23, and GATA4 were consistently linked to hallmark pathways, including immune evasion, angiogenesis, and proliferative signaling. Notably, a substantial subset of regulated genes was not associated with any known pathway, indicating potential novel regulatory mechanisms. Conclusions. This integrative approach highlights the com-plexity of tissue-specific gene regulation mediated by endometriosis-associated variants. Our findings provide a functional framework to prioritize candidate genes and support new mechanistic hypotheses for the molecular pathophysiology of endometriosis. Full article

Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 $\mathsf{\mu }$m. The architecture processes individual 1 cm² patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 $\mathsf{\mu }$m, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 $\mathsf{\mu }$m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 $\mathsf{\mu }$m, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge. Full article

Colorectal cancer is one of the most commonly diagnosed types of cancer and constitutes the second most frequent cancer in women (W) and the third most frequent cancer in men (M). The aim of the study was to determine if environmental exposure to zearalenone (ZEN) (a mycoestrogen) affects the metabolic profile of patients diagnosed with sigmoid colorectal cancer (SCC) and colorectal cancer (CRC) (division based on their location) at hospital admission. Male and female patients who were diagnosed with SCC or CRC and whose blood samples tested positive or negative for ZEN participated in a year-long study. Seventeen patients with symptoms of SCC and CRC, in whom ZEN and its metabolites were not detected in peripheral blood, constituted the patients without ZEN (PWZ) group. The experimental groups comprised a total of 16 patients who were diagnosed with SCC or CRC and tested positive for ZEN but negative for ZEN metabolites. Patients exposed to ZEN were characterized by increased levels of liver enzymes (alanine aminotransferase (ALT) from 5.8 to 18.1 IU/L; aspartate aminotransferase (AST) from 2.8 to 10.7 IU/L) and decrease in the value of the De Ritis ratio (below 1.0), different gamma glutamyl transpeptidase and AST activity, lower albumin (from 0.24 g/dL in M to 0.67 g/dL in W) and total protein levels (from 0.75 to 1.76 g/dL), a decrease in total cholesterol (from 21.6 to 40.3 mg/dL) and triglyceride levels (from 7.8 to 37.2 mg/dL), and lower activity of lipase C (from 28.72 to 64.75 IU/L). The metabolic profile of M and W patients diagnosed with SCC and CRC and exposed to ZEN revealed intensified biotransformation processes in the liver, liver damage, and a predominance of catabolic processes. Full article

Background/Objectives: The metastatic patterns of apical lymph node (ALN) in rectal and sigmoid colon cancer are currently unclear, and there is no consensus on the indications for dissection of ALN. This study aimed to analyze the impact of ALN metastasis on prognosis, determine the metastatic patterns of ALN and provide evidence for indications of ALN dissection in rectal and sigmoid colon cancer. Methods: In this multicenter, retrospective cohort study, patients from five centers with stage I-III rectal or sigmoid colon cancer who underwent laparoscopic radical surgery with ALN dissection without neoadjuvant treatment from January 2015 to December 2019 were enrolled. Results: Among 2809 patients, the positive rate of ALN was 1.9%. The 5-year overall survival and cancer-specific survival rate for patients with metastatic ALN were 37.5% and 41.0%, respectively. ALN metastasis was the independent risk factor for poor prognosis. Tumor size ≥5 cm (OR = 2.32, 95% CI: 1.30–4.13, p = 0.004), signet ring cell cancer/mucinous adenocarcinoma (vs. poor differentiated adenocarcinoma, OR = 0.19, 95% CI: 0.08–0.45, p < 0.001; vs. moderate to well differentiated adenocarcinoma, OR = 0.22, 95% CI: 0.11–0.42, p < 0.001), T4 stage (OR = 1.93, 95% CI: 1.05–3.55, p = 0.034), N2 stage (OR = 8.86, 95% CI: 4.45–17.65, p < 0.001) and radiologic evidence of extramural venous invasion (OR = 1.88, 95% CI: 1.03–3.42, p = 0.040) were independent risk factors for ALN metastasis. The nomogram model developed by these factors achieved a good predictive performance. Conclusions: This research offered insights into the incidence, risk factors, and prognostic significance of apical lymph node metastasis in cases of rectal and sigmoid colon cancer. Additionally, the study furnished empirical support for the criteria guiding ALN dissection. Furthermore, a pragmatic risk assessment model was developed to predict ALN metastasis. Full article

Background: Radiotherapy (RT) is a mainstay treatment for prostate cancer (PC). Accurate delineation of organs at risk (OARs) is crucial for optimizing the therapeutic window by minimizing side effects. Manual segmentation is time-consuming and prone to inter-operator variability. This study investigates the performance of Limbus^® Contour^® (LC), a deep learning-based auto-contouring software, in delineating pelvic structures in PC patients. Methods: We evaluated LC’s performance on key structures (bowel bag, bladder, rectum, sigmoid colon, and pelvic lymph nodes) in 52 patients. We compared auto-contoured structures with those manually delineated by radiation oncologists using different metrics. Results: LC achieved good agreement for the bladder (median Dice: 0.95) and rectum (median Dice: 0.83). However, limitations were observed for the bowel bag (median Dice: 0.64) and sigmoid colon (median Dice: 0.6), with inclusion of irrelevant structures. While the median Dice for pelvic lymph nodes was acceptable (0.73), the software lacked sub-regional differentiation, limiting its applicability in certain other oncologic settings. Conclusions: LC shows promise for automating OAR delineation in prostate radiotherapy, particularly for the bladder and rectum. Improvements are needed for bowel bag, sigmoid colon, and lymph node sub-regionalization. Further validation with a broader and larger patient cohort is recommended to assess generalizability. Full article

Synchronous colorectal cancer (SCRC) is characterized by the simultaneous occurrence of two or more primary colorectal malignancies, diagnosed either preoperatively, intraoperatively, or within six months postoperatively. The rare prevalence of SCRC makes it an uncommon scenario among colorectal malignancies. Since the majority of SCRC patients have been reported to have two concurrent malignancies, triple synchronous malignancies are extremely rare. We report the case of a 65-year-old male individual presenting with a history of abdominal pain, anemia, anorexia, and unintentional weight loss. He was diagnosed with synchronous colorectal cancer with three distinct tumors: two located in the splenic flexure and sigmoid colon, respectively, and another in the rectum that caused partial obstruction. This case highlights the importance of intraoperative evaluation and an appropriate choice of surgical intervention in colorectal cancer. The early identification and proper management of multiple colorectal cancers remain essential for better survival rates. Full article

Background: Endometrial cancer is the most common gynecologic malignancy in developed countries, with obesity recognized as a major risk factor contributing to its incidence. The rising prevalence of obesity has significant implications for treatment planning, particularly in radiation therapy approaches such as high-dose-rate (HDR) vaginal cuff brachytherapy, which is commonly used as adjuvant therapy in early-stage endometrial carcinoma. Body Mass Index (BMI) is a key factor in brachytherapy, as increased adiposity may alter dosimetric parameters, affecting radiation distribution and doses received by organs at risk (OARs). Understanding the correlation between BMI and radiation dose to OARs is essential for optimizing treatment planning and minimizing adverse effects. Identifying dose variations across different BMI categories may help refine patient-specific brachytherapy approaches to ensure both efficacy and safety. Objectives: This study aims to investigate the influence of Body Mass Index (BMI) on the doses received by organs at risk (OAR) during high-dose-rate (HDR) vaginal cuff brachytherapy in patients diagnosed with early-stage endometrial carcinoma. Understanding the relationship between BMI and OAR doses could enhance treatment planning and minimize complications. Methods: We collected brachytherapy data for 242 endometrial cancer patients treated with adjuvant HDR vaginal cuff brachytherapy. The patients were categorized based on their BMI into normal weight, overweight, and obese groups. Dosimetric data were collected for OARs, including the bladder, rectum, and sigmoid colon, and also for dose fractionation, D90%, and the active length of the brachytherapy cylinder. The analysis included comparing the doses received by each organ across different BMI categories using appropriate statistical methods. Results: Preliminary findings indicated a significant variation in the doses to OARs correlating with BMI classifications. Obese patients exhibited slightly higher mean doses to the rectum and sigmoid compared to those with a normal BMI. The statistical analysis demonstrated that as BMI increased, the dose to these organs at risk also tended to increase, suggesting a need for adjusted treatment planning strategies in this population. Conclusions: Obesity is a key concern in endometrial cancer patients, with higher BMI linked to slightly increased doses to the rectum and sigmoid, though treatment remained homogeneously delivered. Future prospective clinical studies are essential to explore the relationship between these dosimetric findings, specifically the correlation between higher BMI, increased doses to organs at risk (OARs), and late treatment-related toxicities. This research is needed to better understand the long-term implications and to optimize therapeutic outcomes. Full article

Background: Timely detection and removal of colonic adenomas are critical for preventing colorectal cancer. Methods: This study analyzed differences in colonic adenoma characteristics based on colonoscopy history by reviewing the medical records of 14,029 patients who underwent colonoscopy between January and June 2020 across 40 primary medical institutions in Korea. Results: Adenoma and advanced neoplasia characteristics varied significantly with colonoscopy history (p < 0.05). In the first-time colonoscopy group, adenomas were more frequent in the sigmoid colon (S-colon) and rectum, with Is features and non-granular laterally spreading tumors. Advanced neoplasia was also more common in the S-colon and rectum, with Is and advanced-type features. In the <5-year group, adenomas were predominantly found in the transverse colon (T-colon) and descending colon (D-colon), with IIa and IIb features. Advanced neoplasia in this group was more frequent in the cecum and T-colon, with IIa and IIb features and laterally spreading tumors. In the ≥5-year group, adenomas were more commonly located in the ascending colon (A-colon) and cecum, with Ip features, while advanced neoplasia was more frequent in the A-colon and D-colon, also with Ip features. Conclusions: Although every segment of the colorectum should be carefully observed regardless of colonoscopy history, these findings suggest that prioritizing specific colonic segments for examination based on colonoscopy history may improve adenoma detection rates and reduce the incidence of colorectal cancer. However, further large-scale, prospective studies are needed to confirm these findings and support their application in clinical practice. Full article

Sigmoid diverticulitis is a common disease characterized by a well-standardized diagnostic approach and treatment. Colorectal cancer is the third most common malignancy worldwide, irrespective of gender. In 2020, CRC global-related mortality rate was estimated at 935,173 cases, with an incidence of 9.3% in men and 9.5% in women. The diagnosis of acute diverticulitis is always made by performing a contrast-enhanced-computed tomography (CT) of the abdomen. Current diagnosis guidelines do not recommend the use of a magnetic resonance imaging (MRI) for further and more precise assessment of a suspected sigmoid diverticulitis diagnosed by CT. Early lower-gastrointestinal (lower-GI) endoscopy is rarely conducted; thus, the diagnosis delay could have a negative impact over the oncological outcome of the disease. Few and scarce data can be found related to this issue, with only a recent Swedish study paying attention towards early identification of neoplastic disease residing on a background of sigmoid diverticulitis, facilitated by MRI. The purpose of this study is to evaluate the feasibility of systematically performing an abdominal MRI included in the primary assessment of acute diverticulitis already diagnosed by CT, in order to argument in favor of an early lower-GI endoscopy where a positive MRI for neoplasia is found. Full article

Colorectal polyps, precursors to colorectal cancer (CRC), require precise identification for appropriate diagnosis and therapy. This study aims to investigate the differences in hematological and inflammatory markers, specifically the CALLY index, HALP score, and immuno-inflammatory indexes, between neoplastic and nonneoplastic polyps. A retrospective cross-sectional study was conducted on 758 patients aged 61.0 ± 11.8 who underwent polypectomy between June 2021 and May 2024. Patients with colorectal adenocarcinoma (n = 22) were excluded. The polyps were classified into neoplastic and nonneoplastic categories based on histopathological evaluation. The study compared the CALLY index, HALP score, and various inflammatory indexes between neoplastic and nonneoplastic polyps. Out of 758 polyps analyzed, 514 were neoplastic, and 244 were nonneoplastic. Neoplastic polyps exhibited significantly lower CALLY and HALP scores (p < 0.05) and higher immuno-inflammatory indexes (p < 0.05) compared to nonneoplastic polyps. Dysplasia status, polyp diameter, and sigmoid colon localization were significant factors in determining neoplastic growth potential. No significant differences were observed in polyp localization in the proximal and distal colon segments or in solitary versus multiple polyps. The CALLY and HALP scores and immuno-inflammatory indexes can serve as valuable markers for distinguishing neoplastic from nonneoplastic polyps. These indexes reflect underlying inflammatory and immune responses, highlighting their potential utility in the early detection and risk stratification of colorectal polyps. Integrating these markers into clinical practice may enhance diagnostic accuracy and improve patient management, leading to timely interventions and better outcomes for individuals at risk of CRC. Full article

Context: Although carcinogenesis is a multi-factorial process, the mutability and the capacity of cells to proliferate are among the major features of the cells that contribute together to the initiation and promotion steps of cancer formation. Particularly, mutability can be quantified by hyper-recombination rate assessed with specific plasmid assay, hypoxanthine-guanine phosphoribosyltransferase (HPRT) mutations frequency rate, or MRE11 nuclease activities. Cell proliferation can be assessed by flow cytometry by quantifying G2/M, G1 arrests, or global cellular evasion. Methods: All these assays were applied to skin untransformed fibroblasts derived from eight major cancer syndromes characterized by their excess of relative cancer risk (ERR). Results: Significant correlations with ERR were found between hyper-recombination assessed by the plasmid assay and G2/M arrest and described a third-degree polynomial ERR function and a sigmoidal ERR function, respectively. The product of the hyper-recombination rate and capacity of proliferation described a linear ERR function that permits one to better discriminate each cancer syndrome. Conclusions: Hyper-recombination and cell proliferation were found to obey differential equations that better highlight the intrinsic bases of cancer formation. Further investigations to verify their relevance for cancer proneness induced by exogenous agents are in progress. Full article

Colonic adenomas are considered a precursor of colorectal cancer. A 75-year-old woman had a history of post-operation left breast cancer. She received an excision when the left chest wall recurred. A later FDG PET/CT scan revealed a focal intense FDG accumulation in the sigmoid, a focal mild FDG uptake in the pericolic lymph node, and a focal increased FDG accumulation in the transverse colon. A delayed FDG PET/CT scan after the per-rectal administration of the laxative-augmented contrast medium revealed a filling defect with persistent FDG uptake in the sigmoid and transverse colon and mild FDG uptake in the pericolic lymph node. In addition, more lesions were observed in the rectum and descending colon. The pathology reports showed sigmoid adenocarcinoma with lymph node metastasis, and adenomas in the transverse colon, descending colon, and rectum. Full article

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis. Full article

The purpose of this work is to design a novel process based on the deep neural network (DNN) process to solve the dynamical human immunodeficiency virus (HIV-1) infection system in cancer patients (HIV-1-ISCP). The dual hidden layer neural network structure using the combination of a radial basis and sigmoid function with twenty and forty neurons is presented for the solution of the nonlinear HIV-1-ISCP. The mathematical form of the model is divided into three classes named cancer population cells (T), healthy cells (H), and infected HIV (I) cells. The validity of the designed novel scheme is proven through the comparison of the results. The optimization is performed using a competent scale conjugate gradient procedure, the correctness of the proposed numerical approach is observed through the reference results, and negligible values of the absolute error are around 10⁻³ to 10⁻⁴. The database numerical solutions are achieved from the Runge–Kutta numerical scheme, and are used further to reduce the mean square error by taking 72% of the data for training, while 14% of the data is taken for testing and substantiations. To authenticate the credibility of this novel procedure, graphical plots using different performances are derived. Full article

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85–87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs. Full article