The feasibility of 18F-FDG PET/CT for predicting pathologic risk status in early-stage uterine cervical squamous cancer

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.

18F-FDG PET/CT semi-quantitative parameters combined with SCC-Ag in predicting lymph node metastasis in stage I-II cervical cancer

Objective

To explore the value of 18F-fluordeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) semi-quantitative parameters of primary tumor combined with squamous cell carcinoma antigen (SCC-Ag) in predicting lymph node metastasis (LNM) of cervical cancer (FIGO 2018 stage I-II).

Materials and Methods

A total of 65 patients with stage I-II cervical cancer underwent 18F-FDG PET/CT were included in our study. Comparing the primary tumor 18F-FDG PET/CT semi-quantitative parameters and SCC-Ag between the LNM group and the non-LNM group. Logistic regression and receiver operating characteristic (ROC) were used to analyze the value of 18F-FDG PET/CT metabolic parameters and SCC-Ag in predicting LNM.

Results

There were 14 and 51 patients were classified as having LNM and NLNM. The semi-quantitative parameters, including the maximum standardized uptake value (SUVmax), the mean standardized uptake value (SUVmean), the peak standardized uptake value (SUVpeak), the total lesion glycolysis (TLG), the metabolic tumor volume (MTV) of the tumor and SCC-Ag were all significantly higher in LNM than in NLNM (SUVmax, 16.07 ± 7.81 vs 11.19 ± 4.73, SUVmean, 9.16 ± 3.48 vs 6.29 ± 2.52, SUVpeak, 12.70 ± 5.26 vs 7.65 ± 3.26, MTV, 22.77 ± 12.36 vs 7.09 ± 5.21, TLG, 211.01 ± 154.25 vs 43.38 ± 36.17, SCC-Ag, 5.39 ± 4.56 vs 2.13 ± 2.50, all p<0.01). Logistic regression analysis showed that TLG was an independent predictor of LNM in stage I-II cervical cancer (OR 1.032, 95% CI 1.013-1.052, p<0.01). Moreover, the predictive value of TLG combined with SUVpeak and SCC-Ag increased and the area under the curve increased compared SUVpeak and SCC-Ag.

Conclusion

18F-FDG PET/CT semi-quantitative parameters and SCC-Ag have promise for assessing LNM in stage I-II cervical cancer. TLG of primary tumor provides independent and increasing values in predicting LNM in stage I-II cervical cancer.

Value of [18F]FDG PET/CT radiomic parameters in the context of response to chemotherapy in advanced cervical cancer

The first-order statistical (FOS) and second-order texture analysis on basis of Gray-Level Co-occurence Matrix (GLCM) were obtained to assess metabolic, volumetric, statistical and radiomic parameters of cervical cancer in response to chemotherapy, recurrence and age of patients. The homogeneous group of 83 patients with histologically confirmed IIIC1-IVB stage cervical cancer were analyzed, retrospectively. Before and after chemotherapy, the advancement of the disease and the effectiveness of the therapy, respectively, were established using [¹⁸F] FDG PET/CT imaging. The statistically significant differences between pre- and post-therapy parameters were observed for SUV_max, SUV_mean, TLG, MTV, asphericity (ASP, p = 0.000, Z > 0), entropy (E, p = 0.0000), correlation (COR, p = 0.0007), energy (En, p = 0.000) and homogeneity (H, p = 0.0018). Among the FOS parameters, moderate correlation was observed between pre-treatment coefficient of variation (COV) and patients' recurrence (R = 0.34, p = 0.001). Among the GLCM textural parameters, moderate positive correlation was observed for post-treatment contrast (C) with the age of patients (R = 0.3, p = 0.0038) and strong and moderate correlation was observed in the case of En and H with chemotherapy response (R = 0.54 and R = 0.46, respectively). All correlations were statistically significant. This study indicates the remarkable importance of pre- and post-treatment [¹⁸F] FDG PET statistical and textural GLCM parameters according to prediction of recurrence and chemotherapy response of cervical cancer patients.

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer

Background

The depth of cervical stromal invasion is one of the important prognostic factors affecting decision-making for early stage cervical cancer (CC). This study aimed to develop and validate a T2-weighted imaging (T2WI)-based radiomics model and explore independent risk factors (factors with statistical significance in both univariate and multivariate analyses) of middle or deep stromal invasion in early stage CC.

Methods

Between March 2017 and March 2021, a total of 234 International Federation of Gynecology and Obstetrics IB1-IIA1 CC patients were enrolled and randomly divided into a training cohort (n = 188) and a validation cohort (n = 46). The radiomics features of each patient were extracted from preoperative sagittal T2WI, and key features were selected. After independent risk factors were identified, a combined model and nomogram incorporating radiomics signature and independent risk factors were developed. Diagnostic accuracy of radiologists was also evaluated.

Results

The maximal tumor diameter (MTD) on magnetic resonance imaging was identified as an independent risk factor. In the validation cohort, the radiomics model, MTD, and combined model showed areas under the curve of 0.879, 0.844, and 0.886. The radiomics model and combined model showed the same sensitivity and specificity of 87.9% and 84.6%, which were better than radiologists (sensitivity, senior = 75.7%, junior = 63.6%; specificity, senior = 69.2%, junior = 53.8%) and MTD (sensitivity = 69.7%, specificity = 76.9%).

Conclusion

MRI-based radiomics analysis outperformed radiologists for the preoperative diagnosis of middle or deep stromal invasion in early stage CC, and the probability can be individually evaluated by a nomogram.