Accuracy Goals in Predicting Preoperative Lymph Node Metastasis for T1 Colorectal Cancer Resected Endoscopically.
Katsuro IchimasaShin-Ei KudoMasashi MisawaKhay-Guan YeohTetsuo NemotoYuta KouyamaYuki TakashinaHideyuki MiyachiPublished in: Gut and liver (2024)
Submucosal invasive (T1) colorectal cancer is a significant clinical management challenge, with an estimated 10% of patients developing extraintestinal lymph node metastasis. This condition necessitates surgical resection along with lymph node dissection to achieve a curative outcome. Thus, the precise preoperative assessment of lymph node metastasis risk is crucial to guide treatment decisions after endoscopic resection. Contemporary clinical guidelines strive to identify a low-risk cohort for whom endoscopic resection will suffice, applying stringent criteria to maximize patient safety. Those failing to meet these criteria are often recommended for surgical resection, with its associated mortality risks although it may still include patients with a low risk of metastasis. In the quest to enhance the precision of preoperative lymph node metastasis risk prediction, innovative models leveraging artificial intelligence or nomograms are being developed. Nevertheless, the debate over the ideal sensitivity and specificity for such models persists, with no consensus on target metrics. This review puts forth postoperative mortality rates as a practical benchmark for the sensitivity of predictive models. We underscore the importance of this method and advocate for research to amass data on surgical mortality in T1 colorectal cancer. Establishing specific benchmarks for predictive accuracy in lymph node metastasis risk assessment will hopefully optimize the treatment of T1 colorectal cancer.
Keyphrases
- lymph node metastasis
- squamous cell carcinoma
- artificial intelligence
- patient safety
- papillary thyroid
- patients undergoing
- risk assessment
- big data
- cardiovascular events
- prognostic factors
- machine learning
- lymph node
- ultrasound guided
- risk factors
- deep learning
- quality improvement
- newly diagnosed
- human health
- cardiovascular disease
- rectal cancer
- ejection fraction
- radiation therapy
- type diabetes
- electronic health record
- coronary artery disease
- chronic kidney disease
- replacement therapy
- clinical evaluation