Blogs
Welcome to our blog section on Artificial Intelligence (AI)! Here, we will explore in-depth one of the fastest and most exciting technological fields of the modern era.
Blogs
26/12/2023
In a groundbreaking stride toward personalized adjuvant treatment for colorectal cancer (CRC), researchers at the Institute for Cancer Genetics and Informatics, Oslo University Hospital, propose a novel paradigm that integrates artificial intelligence (AI)-generated digital pathology tools, traditional histopathological assessment, and circulating tumor DNA (ctDNA) analysis. This innovative approach, outlined by Kerr and colleagues in a recent publication in Nature Reviews Clinical Oncology, holds immense promise in refining treatment stratification for CRC patients’ post-surgery.
For patients battling colorectal cancer, the risk of recurrence within three years post-surgical resection stands at a staggering 80%. The conventional approach to adjuvant therapy relies on histopathological staging, a somewhat rudimentary tool for patient stratification. Recognizing the marginal benefits of adjuvant therapy and the imperative need for more precise methods in patient selection, Professor David Kerr emphasizes the significance of understanding the likelihood of cancer recurrence for tailored adjuvant therapy.
Liquid biopsies, particularly those detecting ctDNA, show clinical utility in the early diagnosis of recurrence, opening avenues for personalized CRC patient management. Despite the cost and delayed assessment inherent in ctDNA analysis post-surgery, its potential to personalize CRC patient care is evident. The proposed paradigm suggests utilizing tissue-based biomarkers for an early pre-selection of treatment, acknowledging the limitations of ctDNA analysis timing.
AI steps into the spotlight as a catalyst for improved patient management. Recent studies, including one featured in The Lancet, have demonstrated the predictive power of AI in CRC patient outcomes. The AI marker, DoMore-v1-CRC, analyzes routine histopathology images to predict the likelihood of cancer-specific death. Integrated with clinicopathological markers, this AI-driven clinical decision support system (CDSS) aids in selecting adjuvant chemotherapy for stage II and III CRC patients without residual disease post-surgery.
Compared to conventional risk stratification, the proposed CDSS identifies a larger group of patients with an excellent prognosis, sparing them unnecessary adjuvant chemotherapy and its associated side effects. The CDSS recommendation, available within days post-surgery, allows high-risk patients to commence treatment promptly. For those identified as low risk, a ctDNA monitoring program is initiated, ensuring timely treatment upon ctDNA detection.
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Source: Institute for Cancer Genetics and Informatics, Oslo University Hospital
Journal Reference: Yang, L., et al. (2023). Personalizing adjuvant therapy for patients with colorectal cancer. Nature Reviews Clinical Oncology. doi.org/10.1038/s41571-023-00834-2.