DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm - Beck, Kyongmin Sarah; Gil, Bomi; Na, Sae Jung; Hong, Ji Hyung; Chun, Sang Hoon; An, Ho Jung; Kim, Jae Jun; Hong, Soon Auck; Lee, Bora; Shim, Won Sang; Park, Sungsoo; Ko, Yoon Ho (Frontiers in Oncology, 2021)
The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning and 3D Convolutional Neural Network (CNN) to predict LVI or pathological nodal involvement on chest CT images. A total of 695 preoperative CT images of resected NSCLC with tumor size of less than or equal to 3 cm from 2008 to 2015 were used to train and validate the DeepCUBIT model using five-fold cross-validation method. We also used tumor size to build a support vector machine (SVM) classifier. Two-hundred and fifty-four out of 695 samples (36.5%) had LVI or nodal involvement. The integrated DeepCUBIT (3D CNN + Tumor size + C/T ratio) model showed sensitivity of 31.8%, speciﬁcity of 89.8%, accuracy of 76.4%, and AUC of 0.759 on external validation cohort. Three single SVM models, using 3D CNN (DeepCUBIT), tumor size or C/T ratio, showed AUCs of 0.717, 0.630 and 0.683, respectively on external validation cohort. DeepCUBIT showed the best single model compared to the models using only C/T ratio or tumor size. In addition, the DeepCUBIT model could signiﬁcantly identify the prognosis of resected NSCLC patients even in stage I. DeepCUBIT using transfer learning and 3D CNN can accurately predict LVI or nodal involvement in cT1 size NSCLC on CT images. Thus, it can provide a more accurate selection of candidates who will beneﬁt from limited surgery without increasing the risk of recurrence.