التفاصيل البيبلوغرافية
| العنوان: |
A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
| المؤلفون: |
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 |
| المساهمون: |
Ou, Yangming, Italian Ministry of Health, Ricerca Corrente 2023 |
| المصدر: |
PLOS ONE ; volume 18, issue 5, page e0285188 ; ISSN 1932-6203 |
| بيانات النشر: |
Public Library of Science (PLoS) |
| سنة النشر: |
2023 |
| المجموعة: |
PLOS Publications (via CrossRef) |
| الوصف: |
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. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1371/journal.pone.0285188 |
| الإتاحة: |
https://doi.org/10.1371/journal.pone.0285188Test |
| حقوق: |
http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.CFBE65D1 |
| قاعدة البيانات: |
BASE |
