MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118350 A) filed by Mr. Sivasubramanian P; Morigadi Vishwashanthi; Leka K K; Veeresh Biradar; C. Illakiyavarshini; Mr. Shashi Prabhat Ranjan; Mrs. J Revathy; Mr. Rebati Sarkar; J. Priyadharshini; Ramireddy Siva; M. Mohamed Abubakkar Siddique; and Thonepudi Hemalatha on October 03, 2026, for Machine Learning-Based Therapeutic Response Prediction And Treatment Optimization System For Lung Cancer.
Inventors include Mr. Sivasubramanian P; Morigadi Vishwashanthi; Leka K K; Veeresh Biradar; C. Illakiyavarshini; Mr. Shashi Prabhat Ranjan; Mrs. J Revathy; Mr. Rebati Sarkar; J. Priyadharshini; Ramireddy Siva; M. Mohamed Abubakkar Siddique; and Thonepudi Hemalatha.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: The present invention relates to a Machine Learning-Based Therapeutic Response Prediction and Treatment Optimization System for Lung Cancer. Existing approaches to lung cancer treatment planning make use of clinical records, diagnostic observations, imaging information, laboratory findings, pathological information, treatment history, and other patient-related information to support assessment of disease and treatment response. However, these sources are often considered separately, while the response of one patient to a particular treatment may differ from that of another patient with apparently similar characteristics. Conventional computer-based systems may also provide patient information or individual predictions without continuously combining treatment response, patient-specific factors, and follow-up observations into an updated assessment. The proposed invention addresses these limitations through an integrated system that collects and organizes patient, disease, treatment, and follow-up information and applies machine learning-based analysis to estimate therapeutic response. The system generates specific indicators including a Patient Response Prediction Score (PRPS), Treatment Suitability Score (TSS), Disease Progression Risk Score (DPRS), Treatment Tolerance Indicator (TTI), and Response Confidence Score (RCS). These indicators are considered together when analysing and comparing available treatment options for an individual patient. The system further receives subsequent observations, including treatment-response and follow-up information, and updates the relevant patient profile and predictions when new information becomes available. The resulting output provides structured decision-support information for review by qualified healthcare professionals. The proposed system may help organize complex patient information, support patient-specific treatment-response assessment, improve comparison of available treatment options, and provide an adaptive approach in which later observations can contribute to subsequent predictions. The system is intended to support clinical decision- making and not independently prescribe or administer treatment. FIG.1
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