Selection of Pathological N0 (pN0) in Clinical IA (cIA) Lung Adenocarcinoma by Imaging Findings of the Main Tumor

Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT

OBJECTIVE

The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients.

METHODS

Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test.

RESULTS

Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05).

CONCLUSION

Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers.

CLINICAL RELEVANCE STATEMENT

Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT.

KEY POINTS

• Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5.

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.