Authors
Valentina Chiappa, Carlotta Caia, Matteo Interlenghi, Biagio Paolini, Simona Palladino, Diletta Costeniero, Marzia Mietta, Francesco Raspagliesi, Christian Salvatore, Isabella Castiglioni, Giovanni Corso, Giorgio Bogani
Published in
European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP). Sep 30, 2026. Epub Sep 30, 2026.
Abstract
To develop and temporally validate a proof-of-concept deep learning model applied to digitized ThinPrep cervical cytology and human papillomavirus (HPV) cotest results for cervical dysplasia identification and triage support. This retrospective single-center study included women undergoing cervical cotesting at Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, between January and September 2023 (development cohort January-July; temporal validation cohort August-September). The model distinguished true-negative cotest cases from HPV-positive low-grade/high-grade squamous intraepithelial lesions; atypical squamous cells of undetermined significance or cannot exclude high-grade lesion, glandular abnormalities, and invasive lesions were excluded. Two convolutional neural network architectures (ResNet50 and DenseNet201) were trained using transfer learning and evaluated by three-fold cross-validation. The best-performing model was validated on an independent temporal cohort (732 image patches; n = 63 women). ThinPrep slides from 337 women generated 3867 image patches for model development. All performance metrics are reported at the image-patch level and not at the patient level. The ResNet50 ensemble achieved the highest patch-level performance (area under the receiver operating characteristic curve: 96%) in temporal validation. At the optimized 30% threshold, sensitivity was 93%, negative predictive value 94%, and accuracy 89%. Reference cotest categories were significantly associated with colposcopic impression (P < 0.001) and histological outcome (P < 0.001 in the development cohort). The model demonstrated high patch-level discriminative ability for the selected binary cotest classification. Although these findings suggest a potential role for artificial intelligence-assisted cytology within screening programs, prospective multicenter studies with patient-level analysis are required before clinical implementation.
PMID:
42825519
Bibliographic data and abstract were imported from PubMed on 02 Oct 2026.
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