Artificial intelligence in the radiologic assessment of ductal carcinoma in situ: a systematic review
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By
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Colin Wu
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Alison Bartak
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Ramaswamy Sharma
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July 14, 2026
Clinical Scorecard: Evaluating the Role of Artificial Intelligence in Radiologic Diagnosis of Ductal Carcinoma In Situ: A Comprehensive Review
At a Glance
| Category | Detail |
|---|
| Condition | Ductal Carcinoma In Situ (DCIS) |
| Key Mechanisms | Artificial intelligence enhances diagnostic accuracy, sensitivity, and specificity in radiologic assessment. |
| Target Population | Patients diagnosed with ductal carcinoma in situ. |
| Care Setting | Radiology and oncology departments utilizing imaging modalities. |
Key Highlights
- DCIS accounts for approximately 25% of new breast cancer diagnoses.
- AI models show AUCs ranging from 0.70 to 0.97, with sensitivities of 80–96% and specificities up to 93%.
- AI has potential applications in detection, classification, and risk stratification of DCIS.
- Mammography is limited by low sensitivity in dense breasts and low-grade lesions.
- Temporal, multiphase, and spatially aware AI models outperform conventional 2D approaches.
Guideline-Based Recommendations
Diagnosis
- Utilize AI technologies to improve detection and classification of DCIS.
Management
- Consider AI-assisted imaging for preoperative risk stratification.
Monitoring & Follow-up
- Implement AI tools for real-time intraoperative margin assessments.
Risks
- Be aware of the 20% to 50% risk of upstaging to invasive cancer post-surgery.
Patient & Prescribing Data
Patients with diagnosed ductal carcinoma in situ requiring imaging assessment.
AI can help differentiate between low-risk and high-risk DCIS cases to avoid overtreatment.
Clinical Best Practices
- Integrate AI tools into routine radiologic assessment of DCIS.
- Ensure comprehensive training for radiologists on AI applications in imaging.
- Adopt standardized protocols for AI utilization in clinical practice.
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