Galip Can Uyar: Can AI Predict Cancer Recurrence Before It Happens?
Galip Can Uyar, Medical Doctor at Etlik City Hospital, shared on X about recent paper by Hyun Ae Jung et al., published in Journal of Clinical Oncology.
“Can AI predict cancer recurrence before it happens?
A new deep-learning model (RADAR CARE) predicts 1-year recurrence after curative surgery in early-stage NSCLC using multimodal clinical and radiologic data.
- 14,177 patients
- AUC: 0.854
- Sensitivity: 86%, Specificity: 71.3%
- Risk score independent of TNM stage
RADAR model uses transformer-based AI to process 64 clinical, molecular and imaging features, providing real-time recurrence risk with actionable thresholds:
- Low risk: RADAR < 0.3 (1-year recurrence <1%)
- Intermediate: 0.3–0.6
- High risk: >0.6 (recurrence ≥5%)
Four dynamic risk patterns suggest how to tailor follow-up and adjuvant therapy.
Implication: Surveillance in early-stage NSCLC can now be personalized, moving beyond rigid TNM-based protocols.”
Title: Deep-Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non–Small Cell Lung Cancer: A Multimodal Approach (RADAR CARE Study)
Authors: Hyun Ae Jung, Daehwan Lee, Boram Park, Kiwon Lee, Ho Yun Lee, Tae Jung Kim, Yeong Jeong Jeon, Junghee Lee, Seong Yong Park, Jong Ho Cho, Yong Soo Choi, Sehhoon Park, Jong-Mu Sun, Se-Hoon Lee, Jin Seok Ahn, Myung-Ju Ahn, Hong Kwan Kim
Read The Full Article at Journal of Clinical Oncology.
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