Attenuation- and Entropy-Based Habitat Imaging for High-Risk Features in Lung Adenocarcinoma Presenting as a Part-Solid Nodule on CT: A Multicenter Study.

Background

Prior studies using habitat imaging for lung nodule characterization have been limited by intermixing of nodule types, insufficient handling of lesion heterogeneity, and incomplete consideration of high-risk histologic features.

Objective

The purpose of this study was to predict high-risk features within lung adenocarcinoma presenting as part-solid nodules on low-dose CT (LDCT) by use of habitat imaging incorporating attenuation and entropy measurements.

Methods

This retrospective study included 781 patients (median age, 58 years; 266 men and 515 women) with 781 resected adenocarcinomas manifesting as part-solid nodules on LDCT from July 2018 to December 2025. Patients from one center formed a training set (n = 578), and those from the three other centers formed an external test set (n = 203). The outcome was high-risk adenocarcinoma, defined as poorly differentiated invasive adenocarcinoma or invasive adenocarcinoma with visceral pleural invasion, spread through air spaces, lymphovascular invasion, or lymph node metastases. K-means clustering was used to determine optimal nodule subregions for maps of attenuation and entropy (reflecting local heterogeneity); attenuation and entropy subregions were integrated to form habitats. Habitat volumes and volume ratios (relative to whole-nodule volumes) were determined. A model incorporating demographic and conventional CT features was constructed by multivariable logistic regression analysis. AUCs were compared using a DeLong test.

Results

The optimal number of clusters for both attenuation and entropy was two (attenuation threshold, -447 HU; entropy threshold, 4.201), yielding four habitats (high attenuation high entropy, high attenuation low entropy, low attenuation high entropy, low attenuation low entropy). In the external test set, the AUC for high-risk adenocarcinoma was significantly greater (p < .05) for high-attenuation low-entropy habitat volume (0.863) than for conventional CT features (nodule diameter, solid-component diameter, consolidation-to-tumor ratio, whole-nodule volume) (range, 0.693-0.809), other habitat features (range, 0.614-0.842), and the conventional model (comprising sex, solid-component diameter, and whole-nodule volume; 0.810). High-attenuation low-entropy habitat volume had sensitivity, specificity, PPV, and NPV of 86.4%, 68.6%, 43.2%, and 94.8%, respectively, in the external test set. An executable software application for the final analytic pipeline and the corresponding source code were made publicly available (github.com/mzi969/Habitat-Imaging-High-Risk-LUADs).

Conclusion

The high-attenuation low-entropy habitat volume outperformed conventional CT features in predicting high-risk histologic characteristics of adenocarcinoma.

Clinical impact

Habitat imaging could inform noninvasive risk stratification and clinical decision-making for part-solid nodules encountered during lung cancer screening.