Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes

• Published in: The journal of pathology. Clinical research Vol. 10; no. 6; p. e70006
• Main Authors: Bogaerts, Joep Ma, Steenbeek, Miranda P, Bokhorst, John-Melle, van Bommel, Majke Hd, Abete, Luca, Addante, Francesca, Brinkhuis, Mariel, Chrzan, Alicja, Cordier, Fleur, Devouassoux-Shisheboran, Mojgan, Fernández-Pérez, Juan, Fischer, Anna, Gilks, C Blake, Guerriero, Angela, Jaconi, Marta, Kleijn, Tony G, Kooreman, Loes, Martin, Spencer, Milla, Jakob, Narducci, Nadine, Ntala, Chara, Parkash, Vinita, de Pauw, Christophe, Rabban, Joseph T, Rijstenberg, Lucia, Rottscholl, Robert, Staebler, Annette, Van de Vijver, Koen, Zannoni, Gian Franco, van Zanten, Monica, de Hullu, Joanne A, Simons, Michiel, van der Laak, Jeroen Awm
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.11.2024
• Subjects: Carcinoma in Situ - diagnosis; Carcinoma in Situ - pathology; Cystadenocarcinoma, Serous - diagnosis; Cystadenocarcinoma, Serous - pathology; Deep Learning; Fallopian Tube Neoplasms - diagnosis; Fallopian Tube Neoplasms - pathology; Female; Humans; Image Interpretation, Computer-Assisted; Observer Variation; Original; Reproducibility of Results; deep learning; computational pathology; serous tubal intraepithelial carcinoma; high‐grade serous carcinoma; histopathology; STIC; artificial intelligence; high-grade serous carcinoma
• ISSN: 2056-4538; 2056-4538
• DOI: 10.1002/2056-4538.70006

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.