A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In...

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Bibliographic Details
Published in:PloS one 2023-05, Vol.18 (5), p.e0285188-e0285188
Main Authors: Bove, Samantha, Fanizzi, Annarita, Fadda, Federico, Comes, Maria Colomba, Catino, Annamaria, Cirillo, Angelo, Cristofaro, Cristian, Montrone, Michele, Nardone, Annalisa, Pizzutilo, Pamela, Tufaro, Antonio, Galetta, Domenico, Massafra, Raffaella
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Biology and Life Sciences
Carcinoma, Non-Small-Cell Lung - diagnostic imaging
Carcinoma, Non-Small-Cell Lung - genetics
Computed tomography
Crops
CT imaging
Data acquisition
Datasets
Diagnosis
Diseases
Engineering and Technology
Histology
Humans
Learning
Lung cancer
Lung cancer, Non-small cell
Lung diseases
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - genetics
Machine Learning
Males
Medical colleges
Medical diagnosis
Medical imaging
Medical prognosis
Medical research
Medical treatment
Medicine and Health Sciences
Medicine, Experimental
Neural networks
Non-small cell lung carcinoma
Patients
Prognosis
Radiomics
Relapse
Research and Analysis Methods
Review boards
Risk
Risk factors
Sensitivity
Small cell lung carcinoma
Support vector machines
Test sets
Tomography
Tomography, X-Ray Computed - methods
Training
Transfer learning
Tumors
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Summary:Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0285188