A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients

Abstract

To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from nonenhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77). Results: A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393-8.769, P<0.001) and external validation cohort (HR=3.029, 95% CI: 1.673-5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS. Conclusion: The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients.

Integrative Nomogram of Computed Tomography Radiomics, Clinical, and Tumor Immune Features for Analysis of Disease-Free Survival of NSCLC Patients with Surgery

“…It is still a long way to go from nomogram to an artificial intelligent model, but such perspective application would greatly help the diagnosis and prolong patients' life span. The deep learning signature-based nomogram would be a robust tool for the prognostic prediction in the resected NSCLC patients [ 27 ].…”

AI/ML advances in non-small cell lung cancer biomarker discovery

“…However, native ML models cannot handle time-to-event data while accommodating censored observations. Reflecting this, we found that ML studies predicting NSCLC/LAD survival mainly formulated the survival analysis as a classification problem and transformed time-to-event data into dichotomized endpoints ( 90 – 94 , 96 , 100 , 102 , 103 , 106 , 108 – 111 , 113 , 116 , 117 , 134 , 135 ). To this end, utilizing Random Survival Forests (RSF) for continuous time-to-event survival prediction and those aiming to identify optimal time-to-event ML models were emerging ( 98 , 99 , 101 , 105 ), but further applications and research in this area are warranted.…”

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

“…DL, a subset of machine learning, is a new diagnostic technology for mining internal information from medical images. DL can be applied to tumor segmentation [ 20 ], prognosis prediction [ 21 , 22 ], and treatment response evaluation [ 23 ] by automatically extracting deep-learned or high-order image features. Among them, convolutional neural network (CNN) is famous for handling image classification tasks [ 24 ]; the three major operations of CNNs are convolution, activation, and pooling, and the entire process can be divided into two steps: the forward computation and the back propagation [ 25 ].…”