Andhra Medical College

Glioblastoma (GBM) remains the most aggressive primary brain tumor in adults, with median overall survival under two years despite maximal resection and chemoradiation. Tumor Treating Fields (TTFields) is a treatment modality that disrupts mitosis and inhibits tumor proliferation with low intensity, alternating electric fields. The landmark EF-14 trial demonstrated significant overall survival (OS) and progression-free survival (PFS) benefits with the addition of TTFields to temozolomide. However, the generalizability of these findings in broader, real-world settings is uncertain. This systematic review and meta-analysis evaluated the pooled impact of TTFields on OS and PFS in newly diagnosed GBM. A systematic search was conducted in PubMed, Embase, and Scopus through September 2025. All studies comparing standard Stupp protocol ± TTFields in newly diagnosed GBM. Outcomes of interest were OS and PFS. For OS and PFS, the pooled hazard ratio was estimated using a random-effects model. Heterogeneity of the effects across the studies was described by Cochran's Q and the I2 statistics. Twelve studies involving 2,761 patients (1,214 TTFields-treated; 1,547 controls) met the inclusion criteria, including one Phase III RCT and eleven retrospective or registry-based cohorts. Pooled analysis showed that TTFields significantly improved OS, HR = 0.68, 95

Haematohidrosis is an exceptionally rare and poorly understood clinical phenomenon characterized by the spontaneous excretion of blood through intact skin or natural orifices, with episodes often reported in association with extreme emotional or psychological stress. Owing to its rarity, the condition often poses significant diagnostic challenges and may be misinterpreted as a bleeding disorder, self-inflicted injury, or factitious illness. We report the case of an 11-year-old boy who presented with a 1-month history of recurrent, painless, self-limiting episodes of bleeding from the eyes, nose, and ears. These episodes were temporally associated with periods of academic and parental stress. Comprehensive hematological, biochemical, and radiological investigations, including coagulation studies and von Willebrand factor assays, were within normal limits, effectively excluding systemic or inherited bleeding disorders. Cytological examination and a positive benzidine test confirmed the presence of blood in the secretions. A temporal association between psychosocial stressors and symptom onset was observed. The patient was managed with propranolol to attenuate sympathetic hyperactivity and cognitive behavioral therapy aimed at stress management and coping strategies. This case highlights the importance of recognizing haematohidrosis as a rare clinical condition in which psychosocial factors may play a contributory role, and emphasizes the role of multidisciplinary management to achieve favorable outcomes.

BACKGROUND:Artificial intelligence (AI) is reshaping oncology at every stage of the cancer care pathway, from population-level screening through molecular diagnosis, treatment planning, and post-treatment surveillance. Despite an exponential growth in AI oncology publications exceeding 5000 peer-reviewed studies annually, a critical and persistent gap separates demonstrated algorithmic performance from genuine patient benefit. Most published evidence derives from retrospective, single-institution studies conducted in curated dataset environments that systematically differ from real-world clinical deployment conditions. This comprehensive review examines the translational maturity of AI applications across 18 major malignancies, providing an evidence-stratified, cross-cancer assessment of where AI has fulfilled, approaches, or remains far from fulfilling its transformative potential in oncological care. METHODS:A structured narrative review was conducted across PubMed/MEDLINE, Embase, IEEE Xplore, and the Cochrane Library, supplemented by regulatory grey literature including FDA 510(k) decision summaries, CE Technical Files, and ClinicalTrials.gov. Search terms combined cancer site-specific terminology with AI methodology terms and translational outcome descriptors. Studies were only included if they applied an AI or machine learning methodology to a defined clinical oncological task, reported a clearly specified performance evaluation, and involved human subjects or human-derived clinical data. Evidence quality was assessed using QUADAS-2, PROBAST, and Cochrane RoB 2. A five-tier translational readiness framework, grounded in the NIH T0-T4 translational spectrum and CONSORT-AI/SPIRIT-AI guidelines, was applied a priori to enable cross-cancer comparison. A rigorous distinction was maintained between diagnostic accuracy and clinical utility, defined as demonstrated impact on clinical decision-making or patient-centered outcomes. RESULTS:Across all 18 malignancies, AI development varied profoundly by cancer type. Breast cancer and prostate cancer (Tier 1) represent the most mature AI ecosystems, with multiple FDA-cleared tools for mammographic screening and digital pathology achieving prospective multi-institutional validation; however, randomized evidence demonstrating reduced cancer-specific mortality remains absent. Lung, hepatocellular, and melanoma AI (Tier 2) have achieved regulatory milestones but face documented performance disparities across demographic subgroups, including DermaSensor's 20.7% specificity in primary care settings and HCC model failures in non-viral disease etiologies. Colorectal, glioma, pancreatic, and ovarian cancers (Tier 3) exhibit technical maturity without clinical clarity: colorectal CADe systems increase adenoma detection but meta-analyses of 18,232 patients across 21 RCTs fail to demonstrate improvement in advanced neoplasia detection or cancer incidence reduction. A full study-level presentation of pooled estimates, confidence intervals, and heterogeneity statistics for each cited randomized evidence base across all cancer types would extend beyond the intended scope and format of this cross-cancer narrative review. Gastric, esophageal, cervical, bladder, head and neck, and endometrial cancers (Tier 4) demonstrate promising single-institutional or geographically restricted results without multi-institutional external validation, particularly notable for cervical cancer AI's transformative potential in low- and middle-income countries constrained by absent regulatory frameworks. Hematologic malignancies, sarcoma, and pediatric solid tumors (Tier 5) face structural barriers, workflow incompatibility in hematopathology, extreme rarity in sarcoma (>70 subtypes, <15,000 US cases annually), and irreducible ethical constraints in pediatric data governance, that cannot be resolved through algorithmic refinement alone. CONCLUSIONS:Oncological AI has not yet fulfilled its clinical promise. Across all five translational tiers, a single finding is consistent: diagnostic accuracy is not a surrogate for patient benefit. AI tools with high sensitivity and specificity have repeatedly failed to demonstrate equivalent reductions in cancer-specific mortality, overdiagnosis, or procedural harm under real-world outcome scrutiny. Simultaneously, documented performance disparities across races, ethnicity, disease etiology, and geographic setting reveal that current AI systems risk amplifying the very health inequities they are positioned to resolve. Bridging this translational gap requires three coordinated systemic shifts: regulatory frameworks mandating post-market outcome surveillance as a condition of clinical clearance; prospective trial designs measuring patient-centered endpoints rather than diagnostic concordance alone; and sustained infrastructure investment in federated data governance, demographically inclusive training datasets, and LMIC-accessible regulatory pathways. AI holds genuine potential to reduce cancer mortality on a global scale-but only if held to the evidentiary and equity standards that the stakes of oncological care demand.