The effect that pathologic and radiologic interpretation of invasive and non-invasive areas in lung adenocarcinoma has on T-stage and treatment

Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5-1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0-I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

Excellent survival of pathological N0 small cell lung cancer patients following surgery

BACKGROUND

Current clinical guidelines recommend surgery only for cT1-2N0M0 small cell lung cancer (SCLC) patients. In light of recent studies, the role of surgery in the treatment of SCLC needs to be reconsidered.

METHODS

We reviewed all SCLC patients who underwent surgery from November 2006 to April 2021. Clinicopathological characteristics were retrospectively collected from medical records. Survival analysis was performed by the Kaplan-Meier method. Independent prognostic factors were evaluated by Cox proportional hazard model.

RESULTS

196 SCLC patients undergoing surgical resection were enrolled. The 5-year overall survival for the entire cohort was 49.0% (95% CI: 40.1-58.5%). PN0 patients had significantly superior survival to pN1-2 patients (p < 0.001). The 5-year survival rate of pN0 and pN1-2 patients were 65.5% (95% CI: 54.0-80.8%) and 35.1% (95% CI: 23.3-46.6%), respectively. Multivariate analysis revealed that smoking, older age, and advanced pathological T and N stages were independently associated with poor prognosis. Subgroup analyses demonstrated similar survival among pN0 SCLC patients regardless of pathological T stages (p = 0.416). Furthermore, multivariate analysis showed factors, including age, smoking history, type of surgery, and range of resection, were not independently prognostic factors for the pN0 SCLC patients.

CONCLUSION

Pathological N0 stage SCLC patients have significantly superior survival to pN1-2 patients, regardless of features, including T stage. Thorough preoperative evaluation should be applied to estimate the status of lymph node involvement to achieve better selection of patients who might be candidate for surgery. Studies with larger cohort might help verify the benefit of surgery, especially for T3/4 patients.