Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses

Lung CT-based multi-lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis

BACKGROUND

Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate them. However, existing radiomic methods mainly focus on specific lesion types, and have limitations in handling the presence of multiple lesion types that vary among different patients.

PURPOSE

We aimed to establish a radiomic model based on multiple lesion types in the patient's CT scans, and analyzed the importance of different lesion types in distinguishing the two diseases.

METHODS

120 NTM-LD and 120 MTB-LD patients were retrospectively enrolled in this study and randomly split into the training (168) and testing (72) sets. A total of 1037 radiomic features were extracted separately for each lesion type. The univariate analysis, least absolute shrinkage, and selection operator were used to select the significant radiomic features. The radiomic signature score (Radscore) from each lesion type was estimated and aggregated to construct the multi-lesion feature vector for each patient. A multi-lesion radiomic (MLR) model was then established using the random forest classifier, which can estimate importance coefficients for different lesion types. The performances of the MLR model and single radomic models were investigated by the receiver operating characteristic curve (ROC). The impact of the predicted lesion importance was also evaluated in subjective imaging diagnosis.

RESULTS

The MLR model achieved an area under the curve (AUC) of 90.2% (95% CI: 86.2% 94.1%) in differentiating NTM-LD and MTB-LD, outperforming the models using specific lesion types following existing radiomic models by 1% to 13%. Among different lesion types, tree-in-bud pattern demonstrated the highest distinguishing value, followed by consolidation, nodules, and lymph node enlargement. Given the estimated lesion importance, two senior radiologists exhibited improved accuracy in diagnosis, with an increased accuracy of 8.33% and 8.34%, respectively.

CONCLUSIONS

This is the first radiomic study to use multiple lesion types to distinguish NTM-LD and MTB-LD. The developed MLR model performed well in differentiating the two diseases, and the lesion types with high importance exhibited the potential to assist experienced radiologists in clinical decision-making.

Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma

PURPOSE

The objective is to establish a radiomics nomogram (Rad-nomogram) using dual-phase enhanced computed tomography (DPE-CT) for the prediction of progression-free survival (PFS) in patients diagnosed with stage IV lung adenocarcinoma (ADC).

METHODS

From DPE-CT scans, radiomic characteristics were retrieved from 133 patients diagnosed with stage IV lung ADC. Clinical data were analyzed using univariate and multivariate Cox regression analyses. The radiomics signature was combined with clinical features employing multivariate Cox analysis in order to develop a Rad-nomogram. The predictive efficiency of the nomogram was evaluated using survival studies, such as Kaplan-Meier curves and Harrell's C-index. The benefits and clinical utility of various models were compared using the net reclassification index (NRI), decision curve analysis (DCA), and integrated discrimination improvement (IDI).

RESULTS

In the test cohort, the C-indexes for the clinical, artery, and vein phase CT models were 0.675, 0.691, and 0.678, respectively. The dual-phase achieved a C-index of 0.731, exceeding the CT model, while the developed nomogram reached a C-index of 0.783. The Kaplan-Meier survival study classified patients into low-risk and high-risk groups related to PFS using the Rad-nomogram (p < 0.05). The Rad-nomogram demonstrated a greater net advantage when compared with clinical and Rad models, as indicated by positive values of the NRI and IDI (ranging from 11.6% to 52.6%, p < 0.05).

CONCLUSION

The Rad-nomogram, employing DPE-CT scans, offers a promising approach to predict PFS in individuals diagnosed with stage IV lung ADC.

Differential diagnosis of benign and lung adenocarcinoma presenting as larger solid nodules and masses based on multiscale CT radiomics

PURPOSE

To develop a better radiomic model for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses based on multiscale computed tomography (CT) radiomics.

MATERIALS AND METHODS

This retrospective study enrolled 205 patients with solid nodules and masses from Center 1 between January 2010 and February 2022 and Center 2 between January 2019 and February 2022. After applying the inclusion and exclusion criteria, we retrospectively enrolled 165 patients from two centers and assigned them to the training dataset (n = 115) or the test dataset (n = 50). Radiomics features were extracted from volumes of interest on CT images. A gradient boosting decision tree (GBDT) was used for data dimensionality reduction to perform the final feature selection. Four models were developed using clinical data, conventional imaging features and radiomics features, namely, the clinical and image model (CIM), the plain CT radiomics model (PRM), the enhanced CT radiomics model (ERM) and the combined model (CM). Model performance was evaluated to determine the best model for identifying benign and lung adenocarcinoma presenting as larger solid nodules and masses.

RESULTS

In the training dataset, the areas under the curve (AUCs) for the CIM, PRM, ERM, and CM were 0.718, 0.806, 0.819, and 0.917, respectively. The differential diagnostic capability of the ERM was better than that of the PRM and the CIM. The CM was optimal. Intermediate and junior radiologists and respiratory physicians achieved improved obviously diagnostic results with the radiomics model. The senior radiologists showed slight improved diagnostic results after using the radiomics model.

CONCLUSION

Radiomics may have the potential to be used as a noninvasive tool for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

BACKGROUND

We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).

METHODS

A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS

Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.

CONCLUSIONS

The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.

RELEVANCE STATEMENT

The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.

KEY POINTS

• We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

Radiomics nomogram for the prediction of Ki-67 index in advanced non-small cell lung cancer based on dual-phase enhanced computed tomography

PURPOSE

To develop a radiomics nomogram based on dual-phase enhanced computed tomography (CT) for predicting the Ki-67 index status in patients with advanced non-small cell lung cancer (NSCLC).

METHODS

137 patients with NSCLC who had undergone dual-phase enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled between January 2020 and December 2022. Clinical and laboratory data were collected, and the patients were categorized based on low or high expression of Ki-67 index, with a cut-off value of 40%. The cohort was randomly divided into a training group (n = 95) and a testing group (n = 42) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select the most valuable radiomics features from the dual-phase enhanced CT images. Subsequently, a nomogram that incorporated the radiomics score and clinical factors associated with Ki-67 index status was established through univariate and multivariate logistic regression analyses. The predictive performance of the nomogram was evaluated using the area under the curve (AUC).

RESULTS

The AUC values of the radiomics features of artery phase and vein phase CT in the testing group were 0.748 and 0.758, respectively. The AUC of the dual-phase enhanced CT was 0.785, and the AUC of the developed nomogram was 0.859, which was higher than those of the radiomics (AUC, 0.785) and clinical models (AUC, 0.736).

CONCLUSIONS

The radiomics nomogram based on dual-phase enhanced CT images provides a promising method for predicting the Ki-67 index status in patients with advanced NSCLC.