MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202521080351 A) filed by Supriya Kishor Narad; and Prof. K. T. V. Reddy, Hinganghat, Maharashtra, on Aug. 25, 2025, for 'a method and system for early detection of non-small cell lungs cancer using image fusion and deep learning.'
Inventor(s) include Supriya Kishor Narad; and Prof. K. T. V. Reddy.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "This invention introduces a method for predicting the non-small cells lungs cancer in early stage. The described implementation represents a significant advancement in the application of computational tools for lung cancer diagnosis, particularly in early detection and recurrence prediction. By combining imaging data, clinical feature scoring, and intuitive visualization, it creates a comprehensive diagnostic assistant that bridges the gap between radiological data and oncological insight. Its modularity, interpretability, and scalability position it as an ideal candidate for real-world deployment in hospitals, screening programs, and follow-up care workflows. Future iterations will further enhance its diagnostic depth through machine learning integration and multi-modal data fusions. The present invention provides a transformative framework for enhancing early diagnosis and prediction. Lung cancer remains one of the leading causes of cancer-related deaths globally. Despite significant advancements in treatment modalities, early detection remains the most powerful tool to improve patient outcomes. However, the challenge lies in accurately identifying malignancies at an early stage when symptoms are often absent or nonspecific. The integration of imaging modalities, data analytics, and decision support tools has emerged as a promising approach to tackle this issue. This invention presents an Al-supported workflow that leverages DICOM imaging and clinical feature-based scoring to classify and assess non-small cell lung cancer (NSCLC) stages, estimate recurrence risk, and flag possible early-stage presentations. It encompasses automated segmentation, quantitative analysis of image characteristics, and an integrated recurrence risk scoring framework-all presented through an interactive user interface compatible with real-time validation in a clinical setting."
Disclaimer: Curated by HT Syndication.
