MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: A multi-center study

MRI-based radiomics models for noninvasive evaluation of lymphovascular space invasion in cervical cancer: a systematic review and meta-analysis

AIM

Aimed to evaluate the diagnostic performance of preoperative MRI-based radiomic models for noninvasive prediction of lymphovascular space invasion (LVSI) in patients with cervical cancer (CC).

MATERIALS AND METHODS

A systematic search of the PubMed, Embase, Web of Science, and Cochrane databases was conducted up to December 21, 2023. The quality of the studies was assessed utilizing the Radiomics Quality Score (RQS) system and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) were computed. The clinical utility was evaluated using the Fagan nomogram. Heterogeneity was investigated and subgroup analyses were conducted.

RESULTS

Eleven studies were included, with nine studies reporting independent validation sets. In the training sets, the pooled sensitivity, specificity, DOR, and AUC of SROC were 0.81 (95% CI: 0.75-0.85), 0.78 (95% CI: 0.73-0.83), 15 (95% CI: 11-20), and 0.86 (95% CI: 0.79-0.92), respectively. For the validation sets, the overall sensitivity, specificity, DOR, and AUC of SROC were 0.79 (95% CI: 0.73-0.84), 0.73 (95% CI: 0.67-0.78), 10 (95% CI: 7-15), and 0.83 (95% CI: 0.71-0.91), respectively. The Fagan nomogram indicated good clinical utility. Subgroup analysis revealed that multi-sequence MRI-based models exhibited superior diagnostic performance compared to single-sequence MRI-based models in validation sets.

CONCLUSION

This meta-analysis highlights the potential diagnostic efficacy of MRI-based radiomic models for predicting LVSI in CC. Nevertheless, large-sample, multicenter studies are still warranted, and improvements in the standardization of radiomics methodology are necessary.

The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis

The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%-87%), specificity of 74% (95% CI=69%-79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82-0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity.This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions

PURPOSE

This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation.

METHODS

This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy.

RESULTS

The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89).

CONCLUSION

Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease

BACKGROUND

Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.

METHODS

In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.

RESULTS

The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.

CONCLUSIONS

The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study

Purpose: This study aimed to establish a deep learning radiomics nomogram (DLRN) based on multiparametric MR images for predicting the response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC (FIGO stage IB-IIIB) who underwent preoperative NACT were enrolled from center 1 (220 cases) and center 2 (independent external validation dataset, 65 cases). Handcrafted and deep learning-based radiomics features were extracted from T2WI, DWI and contrast-enhanced (CE)-T1WI, and radiomics signatures were built based on the optimal features. Two types of radiomics signatures and clinical features were integrated into the DLRN for prediction. The AUC, calibration curve and decision curve analysis (DCA) were employed to illustrate the performance of these models and their clinical utility. In addition, disease-free survival (DFS) was assessed by Kaplan–Meier survival curves based on the DLRN. Results: The DLRN showed favorable predictive values in differentiating responders from nonresponders to NACT with AUCs of 0.963, 0.940 and 0.910 in the three datasets, with good calibration (all p > 0.05). Furthermore, the DLRN performed better than the clinical model and handcrafted radiomics signature in all datasets (all p < 0.05) and slightly higher than the DL-based radiomics signature in the internal validation dataset (p = 0.251). DCA indicated that the DLRN has potential in clinical applications. Furthermore, the DLRN was strongly correlated with the DFS of LACC patients (HR = 0.223; p = 0.004). Conclusion: The DLRN performed well in preoperatively predicting the therapeutic response in LACC and could provide valuable information for individualized treatment.