Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research

Abstract

Background: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T_{1 - 2}N₀M₀ (cT_{1 - 2}N₀M₀) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data.

Methods: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT_{1 - 2}N₀M₀ SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV).

Results: The initial investigation revealed a 33.9% OLM positivity rate in cT_{1 - 2}N₀M₀ SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT_{1 - 2}N₀M₀ SCLC patients.

Conclusions: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT_{1 - 2}N₀M₀ SCLC.

Keywords: Radiomics; Contrast-enhanced computed tomography; Occult lymph node metastases; Small-cell lung cancer.

Fig. 1

Flow diagrams showing the pathways associated with patient inclusion and exclusion. SCLC = small-cell lung cancer. DICOM = Digital Imaging and Communications in Medicine

Fig. 2

Workflow of radiomic analysis

Fig. 3

Demonstration of the radiomic nomogram and ROC curves. (a) A radiomic nomogram incorporating clinical parameters, GTV, and PTV features was constructed. (b, c) ROC curves showing the performance of the GTV model, PTV model, GTV + PTV model, clinical model, and combined model for the prediction of OLM in the training (b) and external validation (c) cohorts

Fig. 4

Decision curve analysis of the training cohort (a) and external validation cohort (b). The calibrations of the GTV + PTV model (c) and combined model (d)