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Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.
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http://dx.doi.org/10.3389/fdgth.2025.1550407 | DOI Listing |
Ann Surg Oncol
September 2025
Division of Surgical Oncology, Department of Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors arising from mesenchymal tissues, with extremity and superficial trunk STS (eSTS) comprising the majority of cases. The management of localized eSTS requires a multidisciplinary approach to optimize oncologic and functional outcomes. This review outlines the natural history, diagnostic workup, and treatment principles for localized eSTS, emphasizing the role of histology-specific considerations in guiding management strategies.
View Article and Find Full Text PDFClin Transl Oncol
September 2025
Department of Radiation Oncology, Vithas La Milagrosa University Hospital, Madrid, 28010, Spain.
This narrative review analyzes current evidence comparing single-session and two-session approaches in Stereotactic Body Radiation Therapy (SBRT) and high-dose-rate (HDR) brachytherapy for localized prostate cancer. These ultra-hypofractionated strategies deliver high-precision ablative doses while minimizing exposure to normal tissues. SBRT regimens with fewer than five fractions show tumor control comparable to conventional treatments, offering reduced treatment burden and increased convenience.
View Article and Find Full Text PDFJ Neurooncol
September 2025
Institute of Medical Biostatistics, Epidemiology, and Informatics (IMBEI), University Medical Center Mainz, Mainz, Germany.
Purpose: Patients diagnosed with high-grade gliomas (HGG) often experience substantial psychosocial dis-tress. However, due to neurological and neurocognitive deficits its assessment remains challenging, and needs remain unmet. We compared a novel face-to-face assessment during doctor-patient conversations with questionnaire-based screening.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
View Article and Find Full Text PDFBr J Cancer
September 2025
Division of Cancer Sciences, School of Medical Sciences, University of Leicester, Leicester, UK.
The incidence of oral cavity cancers in the UK is rising. Asian/Asian British ethnic groups and socioeconomically deprived groups are at highest risk with some evidence of worse disease outcomes in South Asian individuals receiving radiotherapy. This variation in incidence and outcomes underscores the urgent need for action.
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