Summary
This 2020 study describes the development of a machine learning classifier applied to histological image data from patients with peripheral lung squamous cell carcinoma to predict recurrence risk. The approach represents an application of computational pathology to prognostic stratification in thoracic oncology. The work does not directly address farming systems, soil health, nutrient density, or food-system outcomes and appears to have been catalogued in error; it belongs in oncology or medical informatics literature rather than a food systems research database.
UK applicability
The methodological approach (computational histopathology for cancer risk stratification) may be relevant to UK clinical pathology practice and NHS diagnostic algorithms, but the findings are specific to a Japanese patient cohort with peripheral squamous cell carcinoma and do not inform UK agricultural or nutritional policy.
Key measures
Recurrence-free survival, histological image features extracted by machine learning, performance metrics (sensitivity, specificity, area under receiver operating characteristic curve)
Outcomes reported
The study developed and validated a machine learning classifier trained on histological image features to predict recurrence risk in patients with peripheral lung squamous cell carcinoma. The model's performance in stratifying patients by recurrence probability was evaluated against conventional clinicopathological variables.
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