AI Predicts Tumor Response Before Treatment”—With Laughter (and Thanks)

AVS Suresh

Medical & Hemato Oncologist-inventor -Investor

Published Aug 9, 2025

Sometimes science reads like a plot twist… and this article delivers! It highlights an AI model—akin to a medical crystal ball—that predicts whether a tumor will cooperate with treatment before the therapies have even begun.

At its core, the model seems to tap into routine clinical features (like age, albumin levels, inflammation markers, and tumor mutation burden) to estimate whether immunotherapy is likely to work—or if the patient is better off trying something else. It’s called LORIS (Logistic Regression–Based Immunotherapy-Response Score), developed by researchers from the National Cancer Institute and Memorial Sloan Kettering. It’s smart, cost-effective, and based on easy-to-get data—including a humble blood test! ([National Institutes of Health (NIH)][1]).

Why we should all give a big belly-laughing “thank you” to the researchers:

Because who wouldn’t want to skip trial-and-error treatments? This is like having a “skip intro” button on immunotherapy.

LORIS helps avoid unnecessary side effects, saves time, and spares hope-crushing delays—basically, it’s cancer-care’s personal assistant with sass.

It democratizes precision oncology: no more waiting for expensive genomic data; clinicians can predict responses with just six simple variables. ([National Institutes of Health (NIH)][2]).

Funny (but true) gratitude-worthy analogies:

It’s like asking your GPS, “Is this route worth it?” and it instantly says, “Nah, avoid that detour—your patience and sanity will thank you later.”

Imagine going to a fortune teller, and instead of vague tea leaves, you get: “Yes, the treatment’s gonna work—cheers to that!”

Adding a new twist: AI meets HPV-linked cancer care

The plot thickens with another AI advance—this time in precision radiotherapy. Once thought of as unrelated, cervical cancer and certain throat cancers are now joined by a common culprit: the human papillomavirus (HPV). This link, especially in non-smoking men with tonsil and tongue-base tumors, opens the door to biological crossovers in treatment strategies. Recognising this, a team of Indian oncologists has built an AI model to personalise radiation therapy, factoring in not just the size or stage of a tumour, but its biological accessibility—blood vessel density, immune cell infiltration, surrounding fat volume, and even necrosis. In the words of lead researcher Dr AVS Suresh, “It’s not just about hitting the tumour harder; it’s about understanding what’s going on inside.”

The model was trained on data from hundreds of patients, producing strikingly accurate forecasts: over 91% success in predicting shrinkage onset, 90% accuracy in disease control duration, and the ability to flag side effects before they arrive. And in true “grateful but cheeky” fashion, this AI might soon be whispering to doctors: “Hey, you can drop the dose by two Gray, spare the side effect, and still keep the cancer in check.” If proven in larger, multi-centre studies, this could mean less collateral damage from radiation without compromising results—a rare win-win in oncology.

In short: These AI tools don’t just predict—they pre-save time, health, and emotional resilience. From avoiding futile immunotherapy to tailoring radiation doses, the message is clear: the future of cancer care is getting smarter, kinder, and—if the scientists keep this up—maybe even a little funnier. So here’s to the innovators, the code-wranglers, and the clinicians turning gigabytes into gigasmiles. May your algorithms be ever in your favour!