MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641079315 A) filed by Dr. Seeni Mohamed Aliar Maraikkayar; Dr. Tamil Selvi; and Dr. Parisa Beham on June 27, 2026, for Digital Twin-Assisted Cancer Recurrence Prediction System Using Histopathological Image Evolution.
Inventors include Dr. Seeni Mohamed Aliar Maraikkayar; Dr. Tamil Selvi; and Dr. Parisa Beham.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention relates to a Digital Twin-Assisted Cancer Recurrence Prediction System Using Histopathological Image Evolution that integrates artificial intelligence, computational pathology, digital twin technology, predictive analytics, and longitudinal histopathological image modeling to assess the likelihood of cancer recurrence in patients following primary treatment. Cancer recurrence remains one of the major challenges in oncology, often resulting in delayed interventions, increased healthcare costs, and reduced patient survival rates. The proposed system establishes a dynamic digital twin of a patient's tumor microenvironment by utilizing sequential histopathological images, clinical records, molecular biomarkers, genomic profiles, treatment history, and longitudinal follow-up data. Advanced image processing algorithms analyze morphological changes, cellular architecture evolution, nuclear atypia progression, stromal remodeling, vascular alterations, immune cell infiltration patterns, and tumor heterogeneity indicators extracted from serial tissue samples. Machine learning and deep learning models simulate disease progression trajectories and continuously update the patient-specific digital twin based on newly acquired clinical and pathological information. The system predicts recurrence probability, recurrence timing, metastatic potential, and treatment response by comparing individual disease evolution patterns with large-scale reference databases containing annotated cancer progression histories. The invention further incorporates explainable artificial intelligence mechanisms to provide interpretable risk assessments and clinical recommendations for oncologists. Cloud-based infrastructure supports real-time monitoring, telepathology integration, and secure healthcare data management. Automated alerts notify healthcare providers when recurrence risk exceeds predefined thresholds, facilitating early intervention and personalized surveillance strategies. The proposed invention significantly enhances prognostic accuracy, supports precision oncology, reduces diagnostic uncertainty, and enables individualized treatment planning through continuous digital representation of tumor evolution, thereby improving long-term patient outcomes and reducing cancer-related mortality.
Disclaimer: Curated by HT Syndication.
