Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk...

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Vydané v:Bioengineering (Basel) Ročník 12; číslo 4; s. 377
Hlavní autori: Vigdorovits, Alon, Olteanu, Gheorghe-Emilian, Tica, Ovidiu, Pascalau, Andrei, Boros, Monica, Pop, Ovidiu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 02.04.2025
MDPI
Predmet:
Artificial neural networks
Biopsy
Bronchoscopy
Business metrics
Cancer
computational pathology
Computed tomography
Computer applications
Datasets
Deep learning
Evolution
Histopathology
Lesions
Lung cancer
Lung carcinoma
Lungs
Machine learning
Medical imaging
Medical imaging equipment
Medical prognosis
Medical research
Medicine, Experimental
Mutation
Neural networks
Optimization
Pathology
Recall
Squamous cell carcinoma
squamous cell carcinoma in situ
Thoracic surgery
Variables
deep learning
squamous cell carcinoma in situ
computational pathology
ISSN:2306-5354, 2306-5354
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Shrnutí:Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk of overtreatment. The ability to predict the evolution of SCIS lesions can significantly impact patient management. Our study explores the use of computational pathology in predicting the evolution of SCIS. We used a dataset consisting of 112 H&E-stained whole slide images (WSIs) that were obtained from the Image Data Resource public repository. The dataset corresponded to tumors of patients who underwent biopsies of SCIS lesions and were subsequently followed up by bronchoscopy and CT scans to monitor for progression to SCC. We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. The performance of both approaches in predicting progression was assessed. The pathomics-based ridge classifier model obtained an F1-score of 0.77, precision of 0.80, and recall of 0.77. The deep learning model performance was similar, with a WSI-level F1-score of 0.80, precision of 0.71, and recall of 0.90. These findings highlight the potential of computational pathology approaches in providing insights into the evolution of SCIS. Larger datasets will be required in order to train highly accurate models. In the future, computational pathology could be used in predicting outcomes in other preinvasive lesions.
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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering12040377