Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram

Abstract

Background Lung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules. Methods A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer‐Lemeshow test in both the training and validation cohorts. Results A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’ age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793‐0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745‐0.872). The Hosmer‐Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram. Conclusions The established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

“…In recent years, the morbidity of PTMC has dramatically increased. Studies have shown that some PTMCs can be associated with highly aggressive histological variants and even exhibit early localized invasion or lymph node and distant metastasis (36)(37)(38)(39). Unfortunately, the accuracy of diagnosing PTMC is inefficient, resulting in a proportion of patients being mistreated or misdiagnosed.…”

“…To construct the radiomics signature, a LASSO logistic regression model was used to reduce the radiomics features. This method is widely used in discriminating benign and malignant lesions (37,40,41), and it is designed to avoid overfitting (42,43). In our study, 13 radiomics features were finally selected as the most closely related features to the sub-1 cm thyroid lesion status, including 1 shape feature, 6 first order statistics features, 2 gray level dependence matrix (GLDM)-derived texture features, 2 gray level run-length matrix (GLRLM)-derived texture features, and 2 gray level size zone matrix (GLSZM)-derived texture features.…”

Development and Validation of a Computed Tomography–Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer

“…10,11 Radiomics has shown promise for extracting noninvasive radiographic virtual biopsy biomarkers. Landmark discoveries have been observed in nasopharyngeal, 12 lung, 13 breast, 14 and urinary bladder 15 cancers, demonstrating improved predictive values of multiparametric imaging features over conventional imaging metrics. We previously developed and validated a radiomicsbased nomogram to predict preoperative lymph node metastasis in patients with colorectal cancer using radiomics-derived signatures.…”

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

“…Artificial intelligence (AI) is a frontier technology adopting multiple computer algorithms to comprehensively analyze the imaging and clinicopathological data of patients for early identification of malignant nodules and decisionmaking assistance, which has achieved higher accuracy and efficiency than manual identification (9). Though the AI technique has rarely been applied to clinical decisionmaking of lung cancer, recent surge in AI algorithms has showed their potential to accurately screen malignant nodules, predict prognosis and therapeutic effect of lung cancer (10)(11)(12), indicating the potential of AI-assisted decision-making for diagnosis, prognosis, and drug efficacy prediction.…”