Evaluating the added benefit of CT texture analysis on conventional CT analysis to differentiate benign ovarian cysts

Predicting MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography features

Background

Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for MET exon 14 (METex14) skipping mutation can have considerable clinical benefits. This study aimed to predict METex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features.

Methods

This retrospective study included 56 patients with PSC diagnosed by pathology. All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect METex14 skipping mutation status. The patients were divided into two groups: METex14 skipping mutation and nonmutation groups. Overall, 1,316 texture features of the whole tumour were extracted. We also collected 12 clinical and 20 conventional CECT features. After dimensionality reduction and selection, predictive models were established by multivariate logistic regression analysis. Models were evaluated using the area under the curve (AUC), and the clinical utility of the model was assessed by decision curve analysis.

Results

METex14 skipping mutation was detected in 17.9% of PSCs. Mutations were found more frequently in those (I) who had smaller long- or short-axis diameters (P=0.02, P=0.01); (II) who had lower T stages (I, II) (P=0.02); and (III) with pseudocapsular or annular enhancement (P=0.03). The combined model based on the conventional and texture models yielded the best performance in predicting METex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture).

Conclusions

Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the METex14 skipping mutation status in PSC.

Radiomics in the CT diagnosis of ovarian cystic malignancies - a pilot study

Background and aims

The conventional computed tomography (CT) appearance of ovarian cystic masses is often insufficient to adequately differentiate between benign and malignant entities. This study aims to investigate whether texture analysis of the fluid component can augment the CT diagnosis of ovarian cystic tumors.

Methods

Eighty-four patients with adnexal cystic lesions who underwent CT examinations were retrospectively included. All patients had a final diagnosis that was established by histological analysis in forty four cases. The texture features of the lesions content were extracted using dedicated software and further used for comparing benign and malignant lesions, primary tumors and metastases, malignant and borderline lesions, and benign and borderline lesions. Texture features' discriminatory ability was evaluated through univariate and receiver operating characteristics analysis and also by the use of the k-nearest-neighbor classifier.

Results

The univariate analysis showed statistically significant results when comparing benign and malignant lesions (the Difference Variance parameter, p=0.0074) and malignant and borderline tumors (the Correlation parameter, p=0.488). The highest accuracy (83.33%) was achieved by the classifier when discriminating primary tumors from ovarian metastases.

Conclusion

Texture parameters were able to successfully discriminate between different types of ovarian cystic lesions based on their content, but it is not entirely clear whether these differences are a result of the physical properties of the fluids or their appartenance to a particular histopathological group. If further validated, radiomics can offer a rapid and non-invasive alternative in the diagnosis of ovarian cystic tumors.

Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis

OBJECTION

The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features.

METHODS

A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals.

RESULTS

Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later.

CONCLUSION

This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.

Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study

Background

Highlighting a gap in comprehending bone microarchitecture's intricacies using dual-energy X-ray absorptiometry (DXA), this study aims to bridge this chasm by analyzing texture in non-weight bearing regions on axial computed tomography (CT) scans. Our goal is to enrich osteoporosis patient management by enhancing bone quality and microarchitecture insights.

Methods

Conducted at Busan Medical Center from March 1, 2013, to August 30, 2022, 1,320 cases (782 patients) were screened. After applying exclusion criteria, 458 samples (296 patients) underwent bone mineral density (BMD) assessment with both CT and DXA. Regions of interest (ROIs) included spine pedicle's maximum trabecular area, sacrum Zone 1, superior/inferior pubic ramus, and femur's greater/lesser trochanters. Texture features (n=45) were extracted from ROIs using gray-level co-occurrence matrices. A regression model predicted BMD, spotlighting the top five influential texture features.

Results

Correlation coefficients ranged from 0.709 (lowest for total femur BMD) to 0.804 (highest for femur intertrochanter BMD). Mean squared error (MSE) values were also provided for lumbar and femur BMD/bone mineral content (BMC) metrics. The most influential texture features included contrast_32, correlation_32_v, and three other metrics.

Conclusions

By melding traditional DXA and CT texture analysis, our approach presents a comprehensive bone health perspective, potentially revolutionizing osteoporosis diagnostics.

Identification of ovarian endometriotic cysts in cystic lesions of the ovary by amide proton transfer-weighted imaging and R2∗ mapping

AIM

To investigate the value of amide proton transfer weighted (APTw) imaging and R2∗ mapping of cystic fluid in differentiating ovarian endometriotic cysts (OE) from other ovarian cystic (OOC) lesions.

MATERIALS AND METHODS

A total of 42 patients who underwent 3 T pelvic magnetic resonance imaging (MRI) were enrolled. Nineteen lesions were OE and 27 lesions were OOC. The APTw imaging and R2∗ values of the cystic fluid were measured and compared between the two groups using the independent sample t-test or Mann-Whitney U-test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of different parameters. The area under ROC curves (AUCs) was compared using the Delong test. Spearman's correlation analysis was used to assess the correlation between APTw imaging and R2∗ values.

RESULTS

APTw imaging values of OE were lower, while R2∗ values were higher in OE than those in OOC (p=0.001 and < 0.001). The AUCs of APTw imaging and R2∗ values to identify OE from OOC were 0.910 and 0.975. The AUC increased to 0.990 when combining APTw imaging and R2∗ values, yet without a significant difference to the APTw imaging or R2∗ value alone (p=0.229 and 0.082, respectively). APTw imaging values were negatively correlated with R2∗ values (r=-0.522, p<0.001).

CONCLUSION

Both APTw imaging and R2∗ values of OE are significantly different from other ovarian cystic lesions. APTw imaging combined with R2∗ values show excellent diagnostic efficacy to differentiate between OE and OOC.

Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

OBJECTIVE

To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting.

METHODS

Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile.

RESULTS

From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others.

CONCLUSIONS

The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making.

KEY POINTS

• The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.