How can we better predict if a child’s brain tumor will return—before symptoms start? In our new study @NEJM_AI, we used deep learning to analyze longitudinal brain MRIs—learning how scans change over time—to predict tumor recurrence in children. Let’s dive in. 🧠📊 👇 🧵1/

In a pair of new policy review articles, researchers from @BrighamWomens and colleagues introduce an international initiative to bring #AI to clinic for pediatric neuro-oncology response assessments. The studies was published in @TheLancetOncol. Read more:

AI RAPNO releases a blueprint @TheLancetOncol to accelerate clinically reliable AI for pediatric brain tumor imaging. Grateful to work with @anahita_fathi in partnership with global leaders in AI and neuro-oncology @BenjaminKannMD @fangusaro @PennRadiology

Are you driven to use AI to transform patient outcomes in oncology? My lab in the AI in Medicine Program (Mass General Brigham, Harvard Medical School) is seeking Postdoc Fellows to pioneer applications of AI—especially LLMs—in cancer care. More here:

My lab’s 5-year NIH R01 grant, awarded to study gene therapy for hearing loss, was abruptly terminated. I want to share how this action has been incredibly harmful and disruptive, not just to my lab, but to the scientific process itself. 1/15

Lean body mass–based oxaliplatin dosing reduces peripheral neuropathy in stage III colon cancer without compromising survival! ✅ Lower rates of grade ≥2 oxaliplatin-induced peripheral neuropathy ✅ Better quality of life ✅ No impact on relapse-free survival or overall survival

Here it is! KEYNOTE-689 primary publication online now @NEJM. It’s been an incredible journey with DAdkins @SitemanCenter @WUSM. All started in early 2010s / now with @US_FDA approval and a new standard of care! @DanaFarberNews @BrighamWomens @BrighamSurgery

It was a real honor working with @anantm and @CJTsaiMDPhD on this companion piece to our ASCO education session. Exciting things ahead for AI applied to #HNSCC clinical care

🧵1/ Big news in unresectable stage III NSCLC. The InTRist study is the first randomized trial comparing NAC vs. neoadjuvant chemoimmunotherapy (NAC-ICI) prior to definitive chemoradiotherapy (CRT). Early results are promising. Let’s dig in. 🔍 #LungCancer #RadOnc #ASCO25

#ASCO25 #ASCO

Very interesting and thought-provoking discussion. Thank you to my co-panelists and all in the audience for joining. @ASCO #ASCO2025

In a new study, researchers from @BrighamRadOnc and colleagues used an #AI tool to help predict relapse of pediatric brain cancer. The study was published in @NEJM. Read more: https://t.co/982MEIOlfi https://t.co/BadDgcrWvi @BenjaminKannMD

11/ The model and code are open-source 🔗. 🤖Code/Model: https://t.co/Yr60IE4wpj 📰Paper (free access link): https://t.co/pgzoBTG0hG 🗞️Press release:

12/ This work was a multi-institutional effort led by 🌟 Phd student @tak_divyanshu. A huge thank you to all collaborators. @MGBResearchNews @DanaFarberNews @BostonChildrens @wearecbtn @aim_harvard #MedTwitter #AI #Pediatrics #NeuroOncology

11/ The model and code are open-source 🔗. 🤖Code/Model: https://t.co/Yr60IE4wpj 📰Paper (free access link): https://t.co/pgzoBTG0hG 🗞️Press release:

10/ This is the first self-supervised deep learning framework designed for longitudinal brain tumor imaging in kids. The temporal learning framework can be applied to any disease where serial imaging is used—cancer, chronic illness, stroke, etc. We hope to see this explored!

9/ Why it matters: ✅ High-risk patients could initiate early, timely treatment before symptoms ✅ Low-risk patients could be put on less frequent scan surveillance

8/ What does this mean? We can now analyze a patient's historical MRI data and provide a point-of-care prediction of short-term recurrence, i.e. individualized recurrence prediction.

7/ More scans = better predictions. Model performance improved the more MRI time points it had, usually stabilizing around 3–6 prior scans, and this varied depending on the dataset.

6/ Once the model can determine scan order, then comes the real test: Can the model be fine-tuned to predict if a glioma will recur within 1 year? Yes—and impressively. It beat standard models by up to 58% in F1-score across 3 datasets of 715 children and 3,994 MRIs. 📈

5/ The model is first trained to do a pretext task: simply learn to guess whether MRI scans are shown in the right chronological order. This self-supervised trick helps the model learn to focus on temporal patterns within the tumor region—without needing labeled outcomes.