Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics

Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis

OBJECTIVE

We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm.

METHODS

Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients' preoperative MVI status.

RESULTS

The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application.

CONCLUSION

The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value.

CRITICAL RELEVANCE STATEMENT

The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm.

KEY POINTS

• Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI

Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper-Pearson confidence intervals. Fifty patients (mean age 63 +/- 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers' performance.

CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies.

MATERIALS AND METHODS

PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity.

RESULTS

In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs.

CONCLUSION

CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.

Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules

Background

In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) with clinicopathological factors to assess pulmonary nodule classification for benign malignant diagnosis.

Methods

A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled. A total of 2,824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naïve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were used to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad-scores were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled. Six important features were selected from the 2,145 radiomic features extracted from CE T1w and T2w images. The XGBoost classifier model achieved the highest discrimination performance with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance with AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively.

Conclusion

MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to that of the other four models. The nomogram model achieved higher performance with the addition of clinical information.

A multi-objective based radiomics feature selection method for response prediction following radiotherapy

Objective.Radiomics contains a large amount of mineable information extracted from medical images, which has important significance in treatment response prediction for personalized treatment. Radiomics analyses generally involve high dimensions and redundant features, feature selection is essential for construction of prediction models.Approach.We proposed a novel multi-objective based radiomics feature selection method (MRMOPSO), where the number of features, sensitivity, and specificity are jointly considered as optimization objectives in feature selection. The MRMOPSO innovated in the following three aspects: (1) Fisher score to initialize the population to speed up the convergence; (2) Min-redundancy particle generation operations to reduce the redundancy between radiomics features, a truncation strategy was introduced to further reduce the number of features effectively; (3) Particle selection operations guided by elitism strategies to improve local search ability of the algorithm. We evaluated the effectiveness of the MRMOPSO by using a multi-institution oropharyngeal cancer dataset from The Cancer Imaging Archive. 357 patients were used for model training and cross validation, an additional 64 patients were used for evaluation.Main results.The area under the curve (AUC) of our method achieved AUCs of 0.82 and 0.84 for cross validation and independent dataset, respectively. Compared with classical feature selection methods, the AUC of MRMOPSO is significantly higher than the Lasso (AUC = 0.74,p-value = 0.02), minimal-redundancy-maximal-relevance criterion (mRMR) (AUC = 0.73,p-value = 0.05), F-score (AUC = 0.48,p-value < 0.01), and mutual information (AUC = 0.69,p-value < 0.01) methods. Compared to single-objective methods, the AUC of MRMOPSO is 12% higher than those of the genetic algorithm (GA) (AUC = 0.68,p-value = 0.02) and particle swarm optimization algorithm (AUC = 0.72,p-value = 0.05) methods. Compared to other multi-objective feature selection methods, the AUC of MRMOPSO is 14% higher than those of multiple objective particle swarm optimization (MOPSO) (AUC = 0.68,p-value = 0.02) and nondominated sorting genetic algorithm II (NSGA2) (AUC = 0.70,p-value = 0.03).Significance.We proposed a multi-objective based radiomics feature selection method. Compared to conventional feature reduction algorithms, the proposed algorithm effectively reduced feature dimension, and achieved superior performance, with improved sensitivity and specificity, for response prediction in radiotherapy.

Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

OBJECTIVE

To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (Model_AP, Model_VP and Model_Combination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort.

RESULTS

A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of Model_AP, Model_VP and Model_Combination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682-0.948), 0.7485 (95% CI 0.602-0.895), and 0.8772 (95% CI 0.780-0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between Model_AP and Model_Combination (P = 0.0396) and between Model_VP and Model_Combination (P = 0.0465). However, the difference in AUCs between Model_AP and Model_VP was not significant (P = 0.5061). These results demonstrate that Model_Combination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model.

CONCLUSIONS

We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) (P < 0.05); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) (P < 0.05) that was slightly higher for solid and subsolid nodules with a diameter of 5-10 mm (37 cases vs. 28 cases) (P < 0.05). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) (P < 0.05). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.