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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MDPI AG
2025-04-01
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| author | Alon Vigdorovits Gheorghe-Emilian Olteanu Ovidiu Tica Andrei Pascalau Monica Boros Ovidiu Pop |
| author_facet | Alon Vigdorovits Gheorghe-Emilian Olteanu Ovidiu Tica Andrei Pascalau Monica Boros Ovidiu Pop |
| author_sort | Alon Vigdorovits |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbf680ed23e44a02b5c450e5c8fc3236 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-dbf680ed23e44a02b5c450e5c8fc32362025-08-20T03:14:14ZengMDPI AGBioengineering2306-53542025-04-0112437710.3390/bioengineering12040377Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational PathologyAlon Vigdorovits0Gheorghe-Emilian Olteanu1Ovidiu Tica2Andrei Pascalau3Monica Boros4Ovidiu Pop5Department of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, RomaniaDepartment of Pathology, British Columbia Cancer Agency, Vancouver, BC V5Z 4E6, CanadaDepartment of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, RomaniaDepartment of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, RomaniaDepartment of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, RomaniaDepartment of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, RomaniaLung 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.https://www.mdpi.com/2306-5354/12/4/377computational pathologydeep learningsquamous cell carcinoma in situ |
| spellingShingle | Alon Vigdorovits Gheorghe-Emilian Olteanu Ovidiu Tica Andrei Pascalau Monica Boros Ovidiu Pop Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology Bioengineering computational pathology deep learning squamous cell carcinoma in situ |
| title | Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology |
| title_full | Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology |
| title_fullStr | Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology |
| title_full_unstemmed | Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology |
| title_short | Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology |
| title_sort | predicting the evolution of lung squamous cell carcinoma in situ using computational pathology |
| topic | computational pathology deep learning squamous cell carcinoma in situ |
| url | https://www.mdpi.com/2306-5354/12/4/377 |
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