How a Tech Founder Used AI to Navigate a Rare Lymphoma Diagnosis and Avoid Unnecessary Radiotherapy

Conno Christou, a 35-year-old tech founder building his second company, tracked nearly 100 biomarkers annually, optimized his sleep with a Whoop band and Oura ring, and followed protocols from longevity researchers Peter Attia and Rhonda Patrick. His 2025 checkup was the best he had seen in years. Then, after a workout, his arm swelled.

Doctors discovered two blood clots in his veins. During pre-surgery exams, they found an 11-by-11-by-8 centimeter mass behind his sternum. A biopsy confirmed an aggressive form of non-Hodgkin's lymphoma (搜索)搜索该疾病 — a rare diagnosis affecting roughly one in 420,000 people, caused by a random genetic mutation with no connection to lifestyle, diet, or stress. The tumor had existed for only about three months. In three more weeks, it would have reached stage four.

"Lucky in my unluckiness," Christou told TechCrunch from his home in Athens. "It was only found because I went in for something else entirely."

When Two World-Class Doctors Disagree

Christou's first oncologist, a renowned specialist, recommended the lighter of two available chemotherapy regimens. He booked his first infusion three days out. The night before treatment, he sought a second opinion. That doctor recommended the harder regimen — continuous in-hospital infusion, cycling every three weeks across six months — citing Christou's specific pathology.

The lighter treatment carried roughly a 60% success rate for his presentation. The aggressive one brought that number to around 85%. Two world-class doctors had arrived at diametrically opposite recommendations.

"As founders, we hold the wheel," Christou said. "You hear many things. You don't have to follow the first advice."

He did not stop at two opinions. Over the next two days, he gathered 12 opinions in total, reaching out to hematologists and oncologists in the US and abroad, calling in every favor he could. Eleven to one voted in favor of the harder path. He took it.

Treating Chemotherapy as a Data-Driven Sprint

Over six months of treatment, Christou approached chemotherapy the way he approached building a company — as a marathon of sprints, each with a finite cycle and each week filled with data points. He also drew on his mandatory 25-month military service in Cyprus, telling himself to trust the process through six cycles.

He wore his Whoop band throughout and found it remarkably accurate at predicting the days his immune system would bottom out, sometimes flagging them before symptoms arrived. He kept a symptom journal using voice transcription, logging every shift, every side effect, every medication and counter-medication. He narrowed his focus to three variables: sleep, nutrition, and, first and foremost, psychology.

"It moves the needle more than anything," Christou said. "I never asked 'why me' — not once. That question has no useful answer."

How AI Became a Second-Opinion Engine

Christou fed all of his data — blood results, scan data, wearable output, journal entries — into Claude, the AI model from Anthropic. A public opinion poll released in March found that a third of American adults now use chatbots for health information and advice.

Experts urge caution. Danielle Bitterman, clinical lead for data science and AI at Mass General Brigham (搜索)搜索该词条, has told the New York Times that general-purpose chatbots are frequently wrong and "have not been thoroughly evaluated" for personalized diagnoses.

Christou does not disagree. "It didn't replace the doctors," he said, but it "helped me ask the right questions." For a condition as rare as his — one an oncologist might see once a year — access to a model that had absorbed the full body of medical literature was, he said, simply not the same as a Google search.

The model proved critical at the end of treatment. His final PET scan came back ambiguous. His oncologist began discussing a second line of therapy, potentially radiotherapy near his heart and lungs. Christou again did his homework. He read that for this specific lymphoma, the false-positive rate on end-of-treatment PET scans is around 60% — a statistic that still astonishes him.

"It's 2026," he said. "Sixty percent."

He fed all three of his PET scans and his MRI into Claude, which flagged a known but easily overlooked phenomenon: in patients under 40 recovering from this type of lymphoma, the thymus gland can reactivate after chemotherapy, showing up on imaging as what appears to be active disease. Given his age and specific scan characteristics, the model put the probability of that explanation at roughly 90%.

He sought three more opinions. The fourth doctor confirmed it: thymus rebound. No active disease. No radiotherapy needed. He was clear.

A New Perspective on the System

Christou is still unfolding what the last year has meant. He built Keragon (搜索)搜索该词条, his current company, before any of this happened; it is an AI-powered platform that helps medical practices automate administrative operations. But going through the system as a patient gave him new perspective. He watched nurses and doctors buried under tasks that had nothing to do with care. He received the same chemotherapy protocol as an 80-year-old woman, the side effects managed through a cascading chain of additional drugs, each causing problems of their own.

He says he is certain that we will look back at this era of treatment and cringe.

He takes Sundays off now, mostly. He tries to be present — at lunch with friends, at home with his dog, in conversations that might once have felt like a distraction from work. A VC friend told him something years ago that he kept replaying during treatment: Be happy now. He says it is among the hardest things to do and yet he finally appreciates its importance.

"It's not happening in 10 years," he said of what AI can already do for patients willing to use it. "It's happening today."