Apparent diffusion coefficient for prediction of parametrial invasion in cervical cancer: a critical evaluation based on stratification to a Likert scale using T2-weighted imaging

Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis

Background

The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC).

Methods

A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC.

Results

A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively.

Conclusion

MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.

Use of current automatic smoke evacuation system in flexible gastrointestinal endoscopy: Its feasibility and potential usefulness

Objectives

Flexible endoscopy does not have a system that can automatically evacuate surgical smoke generated in the gastrointestinal lumen. We aimed to investigate the feasibility and potential usefulness of automatic smoke evacuation systems in flexible endoscopy.

Methods

[Bench] After surgical smoke generated in the stomach was evacuated by the evacuator, the degree of residual smoke and gastric luminal collapse were evaluated to optimize the evacuator settings. [Animal] Insufflation, suction, and total operation time to complete the protocol of 10 cauterizations of the gastric mucosa were measured in three groups: "manual suction only," "manual suction with automatic evacuation (50% force)," and "manual suction with automatic evacuation (70% force)." The stability of endoscopic visualization and operability was evaluated by 10 endoscopists blinded to those suction settings, and the number of manual suctions, insufflations, and total operation time were measured.

Results

[Bench] The degree of residual smoke and gastric luminal collapse were inversely correlated. [Animal] When the automatic evacuator was partially used, there was no difference in the insufflation time, but the suction time (vs 50%; p = 0.011, vs. 70%; p = 0.011) and total operation time (vs. 50%; p = 0.012, vs. 70%; p = 0.036) were significantly reduced compared to manual operation only. Furthermore, manual suction with automatic evacuation (50% force) significantly improved the stability of endoscopic visualization and operability compared to manual operation only (p = 0.041, p = 0.0085).

Conclusions

The automatic smoke evacuation in flexible gastrointestinal endoscopy was potentially feasible and useful by improving the device setting.

Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

PURPOSE

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

PATIENTS AND METHODS

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

RESULTS

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001-1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020-1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013-15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007-1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378-11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

CONCLUSIONS

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

Application of synthetic magnetic resonance imaging and DWI for evaluation of prognostic factors in cervical carcinoma: a prospective preliminary study

OBJECTIVES

To determine the values of quantitative metrics derived from synthetic MRI (SyMRI) and apparent diffusion coefficient (ADC) in evaluating the prognostic factors of cervical carcinoma (CC).

METHODS

In this prospective study, 74 patients with pathologically confirmed CC were enrolled. Pretreatment quantitative metrics including T1, T2 and ADC values were obtained from SyMRI and diffusion-weighted imaging (DWI) sequences. The values of all metrics were compared for different prognostic features using Student's t-test or Mann-Whitney U-test. The receiver operating characteristic (ROC) curve and multivariate logistic regression analysis were utilized to evaluate the diagnostic performance of quantitative variables.

RESULTS

T1 and T2 values of parametrial involvement (PMI)-negative were significantly higher than those of PMI-positive (p = 0.002 and < 0.001), while ADC values did not show a significant difference. The area under curve (AUC) of T1 and T2 values for identifying PMI were 0.743 and 0.831. Only the T2 values showed a significant difference between the lymphovascular space involvement (LVSI)-negative and LVSI-positive (p < 0.001), and the AUC of T2 values for discriminating LVSI was 0.814. The differences of T1, T2, and ADC values between the well/moderately and the poorly differentiated CC were significant (all p < 0.001). The AUCs of T1, T2 and ADC values for predicting differentiation grades were 0.762, 0.830, and 0.808. The combined model of all metrics proved to achieve good diagnostic performance with the AUC of 0.866.

CONCLUSION

SyMRI may be a potential noninvasive tool for assessing the prognostic factors such as PMI, LVSI, and differentiation grades in CC. Moreover, the overall diagnostic performances of synthetic quantitative metrics were superior to the ADC values, especially in identifying PMI and LVSI.

ADVANCES IN KNOWLEDGE

This is the first study to assess the utility of SyMRI-derived parameters and ADC value in evaluating the prognostic factors in CC.

High-Resolution Magnetic Resonance Imaging (HR-MRI) Imaging Characteristics of Vertebral Artery Dissection with Negative MR Routine Scan and Hypoperfusion in Arterial Spin Labeling

Background

Unruptured vertebral artery dissection (VAD) that causes ischemic infarction may require anticoagulant therapy or other treatments. However, anticoagulation therapy is not recommended for patients without ischemic infarction. To date, there has been no research on the imaging characteristics of patients with ischemic hypoperfusion that have a negative routine MRI scan.

Material/Methods

Patients with suspected VAD were recruited between June 2015 and June 2020 in order to perform high-resolution magnetic resonance imaging (HR-MRI). In total, 26 patients with negative MRI routine scans that underwent arterial spin labeling (ASL) examination were included in the study. The patients were divided into the hypoperfusion group and normal group based on whether hypoperfusion was found in ASL. The clinical features and HR-MRI features between these 2 groups were analyzed.

Results

There were no statistical differences between the hypoperfusion group and normal group based on the patient’s clinical characteristics (P>0.05). According to imaging characteristics between the 2 groups, the effective lumen index and the vertebrobasilar artery minimum angle were statistically significant (P<0.001). Therefore, a vertebrobasilar artery minimum angle <90° was negatively correlated to the posterior circulation ischemia hypoperfusion, with a correlation coefficient of −0.686. The effective lumen index was also negatively correlated to the posterior circulation ischemia hypoperfusion, with a correlation coefficient of −0.671. However, the location of the dissection and other HR-MRI characteristics were not statistically significant between these 2 groups (P>0.05).

Conclusions

The hypoperfusion of posterior circulation ischemia caused by VAD is related to the effective lumen index, as well as the vertebrobasilar artery minimum angle.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Diagnosis of Cervical Cancer With Parametrial Invasion on Whole-Tumor Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined With Whole-Lesion Texture Analysis Based on T2- Weighted Images

Purpose: To evaluate the diagnostic value of the combination of whole-tumor dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and whole-lesion texture features based on T2-weighted images for cervical cancer with parametrial invasion. Materials and Methods: Sixty-two patients with cervical cancer (27 with parametrial invasion and 35 without invasion) preoperatively underwent routine MRI and DCE-MRI examinations. DCE-MRI parameters (K^trans, K_ep, and V_e) and texture features (mean, skewness, kurtosis, uniformity, energy, and entropy) based on T2-weighted images were acquired by two observers. All parameters of parametrial invasion and non-invasion were analyzed by one-way analysis of variance. The diagnostic efficiency of significant variables was assessed using receiver operating characteristic analysis. Results: The invasion group of cervical cancer demonstrated significantly higher K^trans (0.335 ± 0.050 vs. 0.269 ± 0.079; p < 0.001), lower energy values (0.503 ± 0.093 vs. 0.602 ± 0.087; p < 0.001), and higher entropy values (1.391 ± 0.193 vs. 1.24 ± 0.129; p < 0.001) than those in the non-invasion group. Optimal diagnostic performance [area under curve [AUC], 0.925; sensitivity, 0.935; specificity, 0.829] could be obtained by the combination of K^trans, energy, and entropy values. The AUC values of K^trans (0.788), energy (0.761), entropy (0.749), the combination of K^trans and energy (0.814), the combination of K^trans and entropy (0.727), and the combination of energy and entropy (0.619) were lower than those of the combination of K^trans, energy, and entropy values. Conclusion: The combination of DCE-MRI and texture analysis is a promising method for diagnosis cervical cancer with parametrial infiltration. Moreover, the combination of K^trans, energy, and entropy is more valuable than any one alone, especially in improving diagnostic sensitivity.

Diffusion-Weighted Imaging in Oncology: An Update

To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the "functional" information obtained by probing the free diffusivity of water molecules into intra and inter-cellular spaces that in tumors mainly depend on cellularity. Although DWI has not gained much space in some oncologic scenarios, this non-invasive tool is routinely used in clinical practice and still remains a hot research topic: it has been tested in almost all cancers to differentiate malignant from benign lesions, to distinguish different malignant histotypes or tumor grades, to predict and/or assess treatment responses, and to identify residual or recurrent tumors in follow-up examinations. In this review, we provide an up-to-date overview on the application of DWI in oncology.

Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram

PURPOSE

To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).

MATERIALS AND METHODS

All 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated.

RESULTS

For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.

CONCLUSIONS

The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.

KEY POINTS

• No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.

Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy

BACKGROUND

Textural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer.

PURPOSE

To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors.

METHODS

Of 378 patients with Stage1-2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm3) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm3. Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features.

RESULTS

Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. (p < 0.02). In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC = 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC = 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC = 0.794). Combining ADC-radiomic (but not T2-radiomic) and clinico-pathological features improved prediction of recurrence compared to the clinico-pathological model (AUC = 0.916, p = 0.006). Findings were supported by bootstrap re-sampling (n = 1000).

CONCLUSION

Textural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors.

Value of diffusion-weighted and dynamic contrast-enhanced MR in predicting parametrial invasion in cervical stromal ring focally disrupted stage IB-IIA cervical cancers

OBJECTIVES

To compare the effectiveness of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging in detecting parametrial invasion (PMI) in cervical stromal ring focally disrupted stage IB-IIA cervical cancers.

METHODS

Eighty-one patients with cervical stromal ring focally disrupted stage IB-IIA cervical cancers (PMI positive, n = 35; PMI negative, n = 46) who underwent preoperative MRI and radical hysterectomy were included in this study. Preoperative clinical variables and MRI variables were analyzed and compared.

RESULTS

The K_trans (min, mean, 10%, 25%, 50%, 75%, 90%), K_ep (min, 10%, 25%, 50%, 75%, 90%), and V_e (min, 10%, 25%, 50%, 75%, 90%) values of patients with PMI were significantly higher than patients without PMI. The apparent diffusion coefficient (ADC) value did not show statistical difference between the two groups (1.01 ± 0.21 vs. 0.97 ± 0.20 10^-3 mm²/s, p = 0.360). Tumor craniocaudal planes were higher in PMI-positive group than PMI-negative group (35.84 ± 15.39 vs. 29.70 ± 11.78 mm, p = 0.048). Tumor craniocaudal planes combined with K_epmin value showed the highest area under the curve (AUCs) of 0.775, with a sensitivity of 72.7% and a specificity of 71.1% (p = 0.000).

CONCLUSIONS

DCE parameters combined tumor craniocaudal planes may represent a prognostic indicator for PMI in cervical stromal ring focally disrupted IB-IIA cervical cancers.