CONTEXT – Given the escalating cases of cancer, the shortage of specialists poses a significant challenge in curbing fatalities. To address this gap, Mumbai’s Tata Memorial Hospital (TMH), the biggest cancer hospital in India, is turning to artificial intelligence (AI)

By established a ‘Bio-Imaging Bank’ for cancer, the hospital is utilising deep learning to craft a cancer-specific tailored algorithm that aids in early-stage cancer detection. It incorporated data from 60,000 patients into the biobank in the last year

WHAT IS A ‘BIO-IMAGING BANK’, AND HOW DOES AI COME INTO THE PICTURE?

• Aim for project – Create a robust repository encompassing radiology and pathology images, intricately linked with clinical information, outcome data, treatment specifics, and additional metadata
• Strategically designed for the training, validation, and rigorous testing of AI algorithms
• Initially focusing on head neck cancers and lung cancers, with a minimum of 1000 patients for each cancer type, the project aims to surpass the committed patient data for both cancer types by its completion date
• Alongside database creation, the project involves training and testing multiple AI algorithms using the gathered data, addressing medically relevant tasks such as screening for lymph node metastases, nucleus segmentation and classification, biomarker prediction (for instance, HPV in oropharyngeal and EGFR in lung cancer), and therapy response prediction
• The multi-institutional project is funded by the Department of Biotechnology, in collaboration with IIT-Bombay, RGCIRC-New Delhi, AIIMS-New Delhi, and PGIMER-Chandigarh

HOW DOES AI HELP IN EARLY CANCER DETECTION?

• Medical Imaging Analysis – Radiology and Pathology: AI algorithms can analyze medical images from various diagnostic tools such as X-rays, CT scans, MRIs, and pathology slides. These algorithms can detect subtle abnormalities or patterns indicative of early-stage tumors that may be challenging for human eyes to identify.
• Screening Mammography – Breast Cancer Detection: AI can assist radiologists in interpreting mammograms for breast cancer screening. AI algorithms can analyze mammographic images to identify potential areas of concern, reducing the likelihood of false negatives and false positives.
• Histopathology Image Analysis – Tissue Sample Analysis: Pathologists often examine tissue samples under a microscope to diagnose cancer. AI helps in analyzing histopathology images, identifying abnormal cellular structures, and providing insights into the likelihood of cancer presence.
• Liquid Biopsies – Circulating Tumor DNA (ctDNA) Analysis: AI is employed in the analysis of liquid biopsies, which involve the detection of circulating tumor DNA in the blood. AI algorithms can identify genetic mutations and alterations associated with specific cancers, aiding in the early detection of cancer and monitoring treatment response.
• Risk Prediction Models – Patient Data Analysis: AI can analyze patient data, including electronic health records and genetic information, to identify individuals at higher risk for certain types of cancer. This enables targeted screening and surveillance for those at increased risk.
• Symptom Monitoring – Natural Language Processing (NLP): AI-powered chatbots or NLP systems can assist in monitoring and analyzing patient-reported symptoms. Early signs and symptoms of cancer can be identified through these systems, prompting further diagnostic evaluation.
• Integration of Multi-Modal Data – Data Fusion: AI can integrate information from various sources, such as medical imaging, genetic data, and clinical records. This comprehensive approach enhances the accuracy of cancer detection by considering multiple aspects of a patient’s health.
• Speed and Efficiency – Automated Image Analysis: AI can process large volumes of medical images quickly and accurately, providing timely results. This speed is crucial for early detection, allowing for prompt intervention and treatment.
• Assisting Healthcare Professionals: AI serves as a valuable tool for healthcare professionals by providing decision support. It can assist in interpreting complex data and images, helping clinicians make more informed and timely decisions about patient care.

CAN AI HELP REDUCE CANCER FATALITIES IN THE FUTURE

1. Early Detection and Diagnosis:
   • AI algorithms can analyze medical imaging data (such as mammograms, CT scans, and pathology slides) with high precision, aiding in the early detection of cancer.
   • Automated image analysis helps identify subtle abnormalities that may indicate the presence of cancer at an early, more treatable stage.
2. Personalized Treatment Plans:
   • AI can analyze large datasets of patient information, including genomic data, to identify specific genetic mutations and biomarkers associated with different types of cancer.
   • This information can be used to tailor personalized treatment plans, including targeted therapies and immunotherapies, increasing the effectiveness of treatment while minimizing side effects.
3. Drug Discovery and Development:
   • AI accelerates the drug discovery process by analyzing vast datasets and identifying potential drug candidates more efficiently.
   • Predictive modeling helps researchers understand the potential efficacy of drugs and their interactions with specific cancer types.
4. Clinical Decision Support:
   • AI provides decision support to healthcare professionals by analyzing complex medical data and offering insights into treatment options.
   • Clinical decision support systems can enhance the accuracy of diagnoses and treatment decisions, leading to better patient outcomes.
5. Monitoring Treatment Response:
   • AI can assist in monitoring patient responses to cancer treatments by analyzing imaging data, circulating tumor DNA, and other relevant parameters.
   • This allows for timely adjustments to treatment plans based on real-time data, optimizing the chances of success.
6. Robotic Surgery:
   • AI-powered robotic systems enable more precise and minimally invasive surgeries, reducing recovery times and complications associated with traditional surgical procedures.
7. Patient Risk Assessment:
   • AI analyzes patient data to identify individuals at higher risk for specific cancers. This allows for targeted screening and preventive measures, potentially detecting cancer at an earlier and more treatable stage.
8. Remote Patient Monitoring:
   • AI-powered tools can monitor patients remotely, collecting and analyzing data to identify any signs of disease progression or complications.
   • Early detection of issues allows for timely interventions and adjustments to treatment plans.
9. Patient Education and Engagement:
   • AI-driven platforms can provide personalized health information and education to patients, promoting better adherence to treatment plans and lifestyle modifications.

While AI holds great promise in improving cancer care, it’s important to note that these technologies should be integrated responsibly and ethically. Continuous validation, collaboration between AI developers and healthcare professionals, and adherence to regulatory standards are crucial to ensure the safety and effectiveness of AI applications in the field of oncology.