Whole Slide Imaging (WSI) has revolutionized modern pathology by enabling high-resolution digitization of tissue specimens, often exceeding 100,000 × 100,000 pixels, thereby supporting enhanced diagnostic interpretation and telepathology workflows. However, automated analysis of such gigapixel-scale data remains challenging due to computational complexity, staining variability, and morphological heterogeneity across samples. Deep learning-driven segmentation techniques particularly Fully Convolutional Networks (FCN), U-Net, and Mask R-CNN have demonstrated significant advances in tumor detection, cellular boundary delineation, and metastasis identification, with state-of-the-art studies reporting Dice similarity coefficients typically ranging from 0.85 to 0.92 in nuclei segmentation tasks. This review provides a focused and comprehensive overview of segmentation-oriented deep learning methodologies specifically designed for WSI in pathology. We summarize the current workflow including image digitization, patch extraction, data annotation, preprocessing strategies such as stain normalization and ROI enhancement, model selection, post-processing optimization, and clinical integration. By consolidating recent developments and outlining persistent gaps including limitations in labeled datasets, generalizability, hyperparameter sensitivity, and real-time deployment barriers this review offers practical insights to accelerate translation of deep learning segmentation into routine pathology practice. The work is intended as a valuable resource for clinicians, biomedical researchers, and developers engaged in computational pathology and precision diagnostics.
High dose radio iodine administration has passed through the conventional open suction method, with its drawbacks and dangers of spillage and excessive exposure to radiation personnel. This was followed by the closed system vacuum-assisted technique. The latest innovation is the gravity augmented administration, which further reduces exposure to radiation personnel by faster consumption by the patient. This technique is easy using simple and easily available materials.
336 Background: Early integration of palliative care (PC) is recommended in advanced cancer to improve quality of life and reduce aggressive end-of-life (EOL) care. However, these patterns are poorly characterised in real-world settings and are critical targets for quality improvement. Methods: We conducted a retrospective analysis of 250 patients with advanced solid tumors who died at a single tertiary care center between 2024 and 2025. Data extracted included demographics, cancer type, last chemotherapy timing, ICU and ward admissions in the last month of life, PC referral timing, place of death, and EOL care (EOLC) provision. Descriptive statistics were performed. Results: Median age was 53 years (range 21–85), 59% female. Common malignancies included lung (29%), breast (23%), and gastrointestinal cancers (25%). Chemotherapy was administered within one month of death in 57% of patients and within two weeks in 27%. ICU admission in the last month occurred in 81% of patients, and 69% died in the ICU. PC referral done in 66% of cases. Late referrals in 31% of patients hindering proper PC services. EOLC was provided to only 24% of patients; family hesitancy and late presentation were frequent barriers. Conclusions: In this cohort, advanced cancer patients frequently received aggressive care near death, with high rates of late chemotherapy, ICU utilization, and in-ICU deaths. Despite most patients being referred to PC, referrals were often delayed and EOLC was markedly underutilized. These findings highlight a critical gap in the systematic interventions, namely early integration of palliative principles and a potential over-reliance on intensive care at the EOL. Implications: Oncology programs should implement routine PC referral at advanced cancer diagnosis, develop protocols for appropriate ICU admission in terminal illness, and enhance clinician communication training to address family barriers and focus on patient-centered EOL goals and improve the quality of life in advanced cancer.
