Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas

Abstract

Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60–0.88), 0.72 (95% CI 0.57–0.87) and 0.64 (95% CI 0.48–0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61–0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61–0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66–0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma

“…We also found that the high-dimensional features of multiple radiological features could be integrated to further reduce the workload of identification by radiologists. In addition, we compared the results with other studies (32)(33)(34), and the results showed that the evaluation effect of our nomogram was more accurate and robust in the verification set (Table S2).…”

Preoperative CT semantic features predict lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study

“…Previous studies have shown that model-based schemes can make better utilize radiographic information to predict lymph node diseases [31]. Das et al integrated clinical parameters and radiomics features extracted from three ROIs of gross tumor volume (GTV), peritumoral volume (PTV), and LN in different ways to create different nomograms for predicting preoperative LNM in adenocarcinoma, and compared the predictive e ciency of each model [32]. The results showed that the AUC of radiological features based on GTV, PTV and LN in the external veri cation cohort were 0.74,0.72 and 0.64 respectively; the AUC of integrating GTV and PTV (GPTV) was 0.75 in the external validation cohort.…”

Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer

“…developed a model to predict the lymph node status of cT1N0M0 lung adenocarcinoma, either alone or in combination with clinical features. The external validation group yielded an AUC of 0.79 (95% CI, 0.66–0.93) ( 32 ). In our study, the combined GTV+CTV model based on traditional CT imaging also exhibited a comparable predictive ability with an AUC value of 0.845.…”