Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma.

Background: Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.

Methods: In this retrospective study, data from patients with resectable LUAD (stage I-III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59). Tumor and peritumor scores were constructed from preoperative CT radiomics features (shape/intensity/texture). An immune score was derived from the density of tumor-infiltrating lymphocytes (TILs) within the cancer epithelium and stroma on hematoxylin and eosin-stained whole-slide images. A clinical score was constructed based on clinicopathological risk factors. A Cox regression model was employed to integrate these scores, thereby constructing a multimodal nomogram to predict disease-free survival (DFS). The adjuvant chemotherapy benefit rate was subsequently calculated based on this nomogram.

Results: The multimodal nomogram outperformed each of the unimodal scores in predicting DFS, with a C-index of 0.769 (vs 0.634-0.731) in the training dataset, 0.730 (vs 0.548-0.713) in the validation dataset, and 0.751 (vs 0.660-0.692) in the testing dataset. It was independently associated with DFS after adjusting for other clinicopathological risk factors (training dataset: HR=3.02, p<0.001; validation dataset: HR=2.33, p<0.001; testing dataset: HR=2.03, p=0.001). The adjuvant chemotherapy benefit rate effectively distinguished between patients benefiting from adjuvant chemotherapy and those from observation alone (interaction p<0.001). Furthermore, the high-/low-risk groups defined by the multimodal nomogram provided refined stratification of candidates for adjuvant chemotherapy identified by current guidelines (p<0.001). Gene set enrichment analyses using the NSCLC Radiogenomics dataset revealed associations between tumor/peritumor scores and pathways involved in epithelial-mesenchymal transition, angiogenesis, IL6-JAK-STAT3 signaling, and reactive oxidative species.

Conclusion: The multimodal nomogram, which incorporates tumor and peritumor morphology with anti-tumor immune response, provides superior prognostic accuracy compared with unimodal scores. Its defined adjuvant chemotherapy benefit rates can inform individualized adjuvant therapy decisions.