BMC medical informatics and decision making · 2026
AI-assisted tumor board decision-making in pancreatic oncology.
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Mergen Markus, Busch Felix, Schwarberg Benjamin, Koldeweihe Pia, Jungwirth David, Sydlik Jonas, Maurer H Carlo, Makowski Marcus R, Spitzl Daniel, Gassert Florian T
Аннотация
BACKGROUND: Pancreatic cancer requires nuanced, multidisciplinary treatment planning typically conducted within tumor boards. While Large Language Models (LLMs) have shown capabilities in medical reasoning, their ability to approximate complex, integrative decision-making in oncology remains underexplored.
METHODS: This study evaluated the performance of LLaMA 3.3 (70b) in predicting tumor board decisions for newly diagnosed pancreatic cancer patients. Clinical documentation (including free-text imaging reports, pathology findings, and patient history) from 42 first-diagnosis cases discussed in a real-world tumor board was collected. The model was tasked with predicting one of three treatment options: surgical resection (SURG), neoadjuvant chemotherapy (NEO), or palliative therapy (PALL). Four prompting strategies were evaluated: zero-shot, advanced (adv.) zero-shot, Chain-of-Thought (CoT), and few-shot prompting. Performance was assessed using accuracy, micro- and macro-averaged F1 scores, and category-specific recall.
RESULTS: The advanced zero-shot and CoT strategies achieved the highest overall accuracy of 78.6% and a micro-averaged F1 score of 0.786. However, this performance was driven primarily by the correct classification of majority classes (SURG and PALL). Crucially, both high-accuracy strategies failed to identify any of the neoadjuvant therapy candidates (Recall NEO = 0.00; 0/7 cases), systematically misclassifying them as palliative or surgical. While few-shot prompting improved the detection of neoadjuvant cases (Recall NEO = 1.00), it introduced substantial noise, reducing overall accuracy to 56.7%. LLaMA 3.3 (70b) demonstrates high concordance with tumor board decisions for clear-cut surgical or palliative cases but exhibits a critical systematic failure in identifying candidates for neoadjuvant therapy. The high global accuracy masks a significant safety limitation regarding the recognition of complex, intermediate-stage patients.
CONCLUSION: These findings suggest that current LLMs may approximate majority-class decisions but risk overlooking curative treatment pathways in nuanced scenarios, necessitating rigorous oversight and specific adaptation before clinical consideration.
METHODS: This study evaluated the performance of LLaMA 3.3 (70b) in predicting tumor board decisions for newly diagnosed pancreatic cancer patients. Clinical documentation (including free-text imaging reports, pathology findings, and patient history) from 42 first-diagnosis cases discussed in a real-world tumor board was collected. The model was tasked with predicting one of three treatment options: surgical resection (SURG), neoadjuvant chemotherapy (NEO), or palliative therapy (PALL). Four prompting strategies were evaluated: zero-shot, advanced (adv.) zero-shot, Chain-of-Thought (CoT), and few-shot prompting. Performance was assessed using accuracy, micro- and macro-averaged F1 scores, and category-specific recall.
RESULTS: The advanced zero-shot and CoT strategies achieved the highest overall accuracy of 78.6% and a micro-averaged F1 score of 0.786. However, this performance was driven primarily by the correct classification of majority classes (SURG and PALL). Crucially, both high-accuracy strategies failed to identify any of the neoadjuvant therapy candidates (Recall NEO = 0.00; 0/7 cases), systematically misclassifying them as palliative or surgical. While few-shot prompting improved the detection of neoadjuvant cases (Recall NEO = 1.00), it introduced substantial noise, reducing overall accuracy to 56.7%. LLaMA 3.3 (70b) demonstrates high concordance with tumor board decisions for clear-cut surgical or palliative cases but exhibits a critical systematic failure in identifying candidates for neoadjuvant therapy. The high global accuracy masks a significant safety limitation regarding the recognition of complex, intermediate-stage patients.
CONCLUSION: These findings suggest that current LLMs may approximate majority-class decisions but risk overlooking curative treatment pathways in nuanced scenarios, necessitating rigorous oversight and specific adaptation before clinical consideration.