Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.

PURPOSE

Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists.

MATERIALS AND METHODS

A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity.

RESULTS

No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05).

CONCLUSIONS

The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.