[Pseudodominant inheritance of spinal muscular atrophy--father and son suffering from SMA].

BACKGROUND

cancer registries (CRs) are essential tools for oncological surveillance. The accurate coding of topography and morphology (through ICD-O-3 coding), traditionally performed manually, is complex and time-consuming. Artificial Intelligence (AI) offers new opportunities to automate this process, overcoming the limitations of existing algorithms, which often focus only on topography.

OBJECTIVES

to develop an AI-based algorithm capable of automatically assigning the combined topography-morphology (topo-morpho) code from a synthetic clinical pathway expressed in natural language (LN-PDTA).

DESIGN

retrospective observational study based on integrated registry and healthcare administrative data. Deterministic record linkage was performed among the CR, administrative databases, and pathology reports (AP), considering clinical events within ±180 days from incidence date. Clinical information (diagnoses, pharmacological therapies, surgical procedures, causes of death, pathology codes) was transformed into chronological clinical tokens concatenated into a single string. The target variable was the combined topo-morpho code assigned by registry coders. An LSTM neural network (embedding=64, hidden=128) was trained to learn token sequences.

SETTING AND

PARTICIPANTS

incident cancer cases recorded by the Cancer Registry of the Agency for Health Protection of the Metropolitan Area of Milan in 2017-2018; multiple, benign, and uncertain tumors were excluded.  Main outcome measures: accuracy in the prediction of topography, morphology, and combined topo-morpho. Precision, recall, and F1 score at different confidence thresholds. Secondary analysis for high-incidence cancer sites and identification of the most predictive tokens and information sources.

RESULTS

the dataset included 34,168 cases, split 80:20 into training and test sets. On the test set, the model achieved 89% accuracy for topography prediction, 59% for morphology, and 56% for the combined topo-morpho classification. Performances were better for highly frequent sites (breast 73%; colorectal 61%). For lung and prostate cancers, accuracy for topography reached 94% and 98%, respectively. The most predictive tokens and information sources were identified: pathology reports, mortality data, and surgical procedures for topography; pathology reports, hospital discharge diagnoses, and mortality for morphology.

CONCLUSIONS

the LN-PDTA-based neural network approach shows promising results for the most frequent topographies and morphologies, thus enabling automatic coding of a fair number of cases, reducing manual coding time and supporting more efficient cancer registry operations.