Predicting lymph node recurrence in cT1‐2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists

• Published in: The journal of pathology. Clinical research 2024-09, Vol.10 (5), p.e12392-n/a
• Main Authors: Adachi, Masahiro, Taki, Tetsuro, Kojima, Motohiro, Sakamoto, Naoya, Matsuura, Kazuto, Hayashi, Ryuichi, Tabuchi, Keiji, Ishikawa, Shumpei, Ishii, Genichiro, Sakashita, Shingo
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Artificial Intelligence; Cancer; Carcinoma, Squamous Cell - pathology; Cell culture; Datasets; Dissection; Female; Gene expression; Humans; Inflammation; Lymph nodes; Lymph Nodes - pathology; lymphatic metastasis; Lymphatic Metastasis - pathology; Lymphatic system; Machine learning; Male; Medical prognosis; Metastasis; Middle Aged; Neoplasm Recurrence, Local - pathology; Neoplasm Staging; Neural networks; Original; Pathologists; pathology; Prediction models; Predictive Value of Tests; Squamous cell carcinoma; Squamous Cell Carcinoma of Head and Neck - pathology; Support vector machines; Tomography; Tongue; tongue neoplasms; Tongue Neoplasms - pathology; Tumors
• ISSN: 2056-4538; 2056-4538
• DOI: 10.1002/2056-4538.12392

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1‐2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1‐2, N0 (cT1‐2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1‐2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI‐extracted information from whole slide images (WSIs), human‐assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non‐recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non‐recurrence cases. The model integrating AI‐extracted histopathological and human‐assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1‐2N0 tongue SCC.