Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules

Differentiating Lung Adenocarcinoma from Tuberculous Nodules in HIV/AIDS Patients Using Preoperative CT-Based Intratumoral and Peritumoral Radiomics Combined with Clinical Features

Purpose

This study aimed to develop and validate a preoperative CT-based radiomics nomogram model incorporating intratumoral and peritumoral features to accurately differentiate lung adenocarcinoma (LUAD) from pulmonary tuberculosis (PTB) nodules in HIV/AIDS patients.

Patients and Methods

This retrospective study analyzed clinical and CT imaging data from 187 hIV/AIDS patients (84 with LUAD and 103 with PTB) treated at the Fourth People's Hospital of Nanning. Patients were randomly divided into training and validation cohorts in a 7:3 ratio. Radiomics features were extracted from both the intratumoral region and a 2 mm peritumoral region, then combined with clinical factors (eg, fever, C-reactive protein levels, and cardiac disease) to develop multiple predictive models, including clinical model, intra model, peri 2mm model, fusion model, and combined model (which integrates clinical and fusion models). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and other metrics.

Results

The combined model achieved the highest AUC in both the training (0.978) and validation cohorts (0.969) cohorts, significantly outperforming the other models while mitigating the overfitting observed in the clinical model. Hosmer-Lemeshow (HL) tests, Integrated Discrimination Improvement (IDI), Net Reclassification Index (NRI), and decision curve analysis (DCA) confirmed its superior performance.

Conclusion

The CT-based radiomics nomogram model, intratumoral and peritumoral radiomics features, enables accurate differentiation between LUAD and PTB in HIV/AIDS patients, providing a non-invasive tool for preoperative diagnosis.

radMLBench: A dataset collection for benchmarking in radiomics

BACKGROUND

New machine learning methods and techniques are frequently introduced in radiomics, but they are often tested on a single dataset, which makes it challenging to assess their true benefit. Currently, there is a lack of a larger, publicly accessible dataset collection on which such assessments could be performed. In this study, a collection of radiomics datasets with binary outcomes in tabular form was curated to allow benchmarking of machine learning methods and techniques.

METHODS

A variety of journals and online sources were searched to identify tabular radiomics data with binary outcomes, which were then compiled into a homogeneous data collection that is easily accessible via Python. To illustrate the utility of the dataset collection, it was applied to investigate whether feature decorrelation prior to feature selection could improve predictive performance in a radiomics pipeline.

RESULTS

A total of 50 radiomic datasets were collected, with sample sizes ranging from 51 to 969 and 101 to 11165 features. Using this data, it was observed that decorrelating features did not yield any significant improvement on average.

CONCLUSIONS

A large collection of datasets, easily accessible via Python, suitable for benchmarking and evaluating new machine learning techniques and methods was curated. Its utility was exemplified by demonstrating that feature decorrelation prior to feature selection does not, on average, lead to significant performance gains and could be omitted, thereby increasing the robustness and reliability of the radiomics pipeline.

The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules

OBJECTIVE

Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.

METHODS

A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.

RESULTS

Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.

CONCLUSION

The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients

Background

The occurrence of bone metastasis (BM) will seriously shorten the survival time of lung adenocarcinoma patients and aggravate the suffering of patients. Computed tomography (CT)-based clinical radiomics nomogram may help clinicians stratify the risk of BM in lung adenocarcinoma patients, thereby enabling personalized individualized clinical decision making.

Methods

A total of 501 patients with lung adenocarcinoma from March 2017 to March 2019 were enrolled in the study. Based on plain chest CT images, 1130 radiomics features were extracted from each lesion. One-way analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO) algorithm were used for radiomics features selection. Univariate and multivariate analyses were used to screen for clinical characteristics and identify independent predictors of BM. Three models (radiomics model, clinical model and combined model) were constructed to predict BM in lung adenocarcinoma patients. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the three models. The DeLong test was used to compare the performance of the models.

Results

Finally, the clinical model for predicting BM in lung adenocarcinoma patients was constructed based on 5 independent predictors: cytokeratin 19-fragments (CYFRA21-1), stage, Ki-67, edge, and lobulation. The radiomics model was constructed based on 5 radiomics features. The combined model incorporating clinical independent predictors and radiomics was constructed. In the validation cohort, the area under the curve (AUC) of the clinical model, radiomics model and combined model was 0.824, 0.842 and 0.866, respectively. Delong test showed that in the training cohort, the AUC values of the radiomics model and the combined model were statistically different (P=0.03), and the AUC values of the other models were not statistically different. DCA showed that the nomogram had a highest net clinical benefit.

Conclusions

The CT-based clinical radiomics nomogram can be used as a non-invasive and quantitative method to help clinicians stratify the risk of BM in patients with lung adenocarcinoma, thereby enabling personalized clinical decision making.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

BACKGROUND

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

METHODS

A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.

RESULTS

In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).

CONCLUSIONS

The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.

Coccidioidomycosis Granulomas Informed by Other Diseases: Advancements, Gaps, and Challenges

Valley fever is a respiratory disease caused by a soil fungus, Coccidioides, that is inhaled upon soil disruption. One mechanism by which the host immune system attempts to control and eliminate Coccidioides is through granuloma formation. However, very little is known about granulomas during Coccidioides infection. Granulomas were first identified in tuberculosis (TB) lungs as early as 1679, and yet many gaps in our understanding of granuloma formation, maintenance, and regulation remain. Granulomas are best defined in TB, providing clues that may be leveraged to understand Coccidioides infections. Granulomas also form during several other infectious and spontaneous diseases including sarcoidosis, chronic granulomatous disease (CGD), and others. This review explores our current understanding of granulomas, as well as potential mechanisms, and applies this knowledge to unraveling coccidioidomycosis granulomas.