Pre-coding skin cancer from free-text pathology reports using noise-robust neural networks

Population-based cancer registries receive numerous free-text pathology reports from which cancer cases are manually coded according to international standards. Skin cancer is the most frequent cancer in Caucasian populations, and its incidence is increasing. We developed an AI-based method to identify skin cancer, locate relevant key terms in pathological reports, and suggest coding for the main clinical variables.

We explored multiple neural network architectures and found out that convolutional neural networks with customised noise-robust loss functions offer the best performance for identifying cancer types and pre-coding subsite, morphology, behaviour, grade, laterality, and first line of treatment of skin cancer cases. Previously registered cases were used as training data. We additionally applied an attention mechanism to extract and highlight reports' key diagnostic terms. These highlights facilitate human review of pre-coding results. We evaluated performance of the method by using manually coded cases in a separate test set.

The accuracies of detecting skin cancer types were 0.98-0.99, and F1 scores 0.93-0.96. Pre-coding accuracy and weighted F1 score were: ICD-O subsite (4 digits): 0.89-0.91 and 0.89-0.91, morphology (4 digits): 0.61-0.90 and 0.63-0.89, morphology (3-digits): 0.86-0.98 and 0.89-0.98, tumour behaviour: 0.96-0.98 and 0.96-0.98, laterality: 0.99 and 0.98-0.99. Also, accuracy (0.96) and weighted F1 score (0.96) for the grade were estimated for squamous cell carcinoma (SCC) of the skin, and treatments for SCC and melanoma (accuracies 0.84 and 0.87, weighted F1 scores and 0.82 and 0.87). The extracted key words matched ICD-O code descriptions with high precision.

We piloted our method in the Vaud Cancer Registry, Switzerland. It was able to identify and pre-code skin cancer cases efficiently and find correct key terms in reports. Medical coders found pre-coding useful and time saving. Integration of the method in the registry document workflow and its extension to other cancer types are intended.