MD Anderson researchers developed the CIPHER AI model to predict pneumonitis in lung cancer patients using baseline CT scans, outperforming existing models.
Key Details
- 1CIPHER is a deep learning AI model based on CT imaging for lung cancer patients starting immunotherapy.
- 2The model was trained on over 590,000 CT slices from 2,500 patients.
- 3Internal validation involved 93 patients, while external validation used a cohort of 116 patients from Johns Hopkins.
- 4CIPHER achieved AUC values between 0.77 and 0.85, outperforming clinical and radiomics models.
- 5In external validation, CIPHER had an AUC of 0.83 and balanced accuracy of 81.7%, with higher specificity than the radiomics comparator.
- 6Prospective studies are still needed for broader clinical implementation.
Why It Matters
Identifying patients at risk for pneumonitis before immunotherapy could enable early intervention and tailored monitoring, potentially improving outcomes and patient safety in oncologic care. This AI model exemplifies the potential of imaging-based predictive analytics in personalizing cancer treatment.
Source
AuntMinnie
Related News
•Radiology Business
AI Model Predicts Organ Failure in Acute Pancreatitis via CT Imaging
A new AI tool using CT imaging predicts organ failure in acute pancreatitis patients earlier than standard methods.
•Cardiovascular Business
AI-Driven CT Analysis Assesses Cardiovascular Aging and Risk
Researchers developed an AI model that uses routine CT scans to estimate a person's cardiovascular age.
•Radiology Business
a2z Radiology AI’s Abdo-Triage Cleared for Expanded CT Findings
FDA has cleared a2z Radiology AI's Abdo-Triage tool to triage 10 additional findings on abdomen-pelvis CT scans.