Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study

Correlation Analysis of Apparent Diffusion Coefficient Histogram Parameters and Clinicopathologic Features for Prognosis Prediction in Uveal Melanoma

Purpose

To investigate the correlation between apparent diffusion coefficient (ADC) histograms and high-risk clinicopathologic features related to uveal melanoma (UM) prognosis.

Methods

This retrospective study included 53 patients with UM who underwent diffusion-weighted imaging (DWI) between August 2015 and March 2024. Axial DWI was performed with a single-shot spin-echo echo-planar imaging sequence. ADC histogram parameters of ADCmean, ADC50%, interquartile range (IQR), skewness, kurtosis, and entropy were obtained from DWI. The relationships between histogram parameters and high-risk clinicopathological characteristics including tumor size, preoperative retinal detachment, histological subtypes, Ki-67 index, and chromosome status, were analyzed by Spearman correlation analysis, Mann-Whitney U test, or Kruskal-Wallis test.

Results

A total of 53 patients (mean ± SD age, 55 ± 15 years; 22 men) were evaluated. The largest basal diameter (LBD) was correlated with kurtosis (r = 0.311, P = 0.024). Tumor prominence (TP) was correlated with entropy (r = 0.581, P < 0.001) and kurtosis (r = 0.273, P = 0.048). Additionally, significant correlations were identified between the Ki-67 index and ADCmean (r = -0.444, P = 0.005), ADC50% (r = -0.487, P = 0.002), and skewness (r = 0.394, P = 0.014). Finally, entropy was correlated with monosomy 3 (r = 0.541, P = 0.017).

Conclusions

The ADC histograms provided valuable insights into high-risk clinicopathologic features of UM and hold promise in the early prediction of UM prognosis.

Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.

The assessment of pathological response to neoadjuvant chemotherapy in muscle-invasive bladder cancer patients with DCE-MRI and DWI: a systematic review and meta-analysis

OBJECTIVE

The purpose of this meta-analysis was to determine the value of dynamic contrast-enhanced-MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in evaluating the pathological response of muscle invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC), and further indirectly compare the diagnostic performance of DCE-MRI and DWI.

METHODS

Literatures associated to DCE-MRI and DWI in the evaluation of pathological response of MIBC to NAC were searched from PubMed, Cochrane Library, web of science, and EMBASE databases. The quality assessment of diagnostic accuracy studies 2 tool was used to assess the quality of studies. Pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curves (AUC) with their 95% confidence intervals (CIs) were calculated to evaluate the diagnostic performance of DCE-MRI and DWI in predicting the pathological response to NAC in patients with MIBC.

RESULTS

There were 11 studies involved, 6 of which only underwent DCE- MRI examination, 4 of which only underwent DWI examination, and 1 of which underwent both DCE- MRI and DWI examination. The pooled SE, SP, PLR, NLR, DOR of DCE-MRI were 0.88 (95% CI: 0.78-0.93), 0.88 (95% CI: 0.67-0.96), 7.4 (95% CI: 2.3-24.2), 0.14 (95% CI: 0.07-0.27), and 53 (95% CI: 10-288), respectively. The pooled SE, SP, PLR, NLR, DOR of DWI were 0.83 (95% CI: 0.75-0.88), 0.88 (95% CI: 0.81-0.93), 7.1 (95% CI: 4.3-11.7), 0.20 (95% CI: 0.14-0.28), and 36 (95% CI:18-73), respectively. The AUCs of SROC curve for DCE-MRI and DWI were 0.93 (95% CI: 0.91-0.95) and 0.92 (95% CI: 0.89-0.94), respectively. There were no significant differences between DWI and DCE-MRI for SE, SP, and AUC.

CONCLUSION

This meta-analysis demonstrated high diagnostic performance of both DCE-MRI and DWI in predicting the pathological response to NAC in MIBC. DWI might be a potential substitute for DCE-MRI, with no significant difference in diagnostic performance between the two. However, caution should be taken when applying our results, as our results were based on indirect comparison.

ADVANCES IN KNOWLEDGE

No previous studies have comprehensively analysed the value of DCE-MRI and DWI in evaluating the pathological response to NAC in MIBC. According to the current study, both DCE-MRI and DWI yielded high diagnostic performance, with the AUCs of 0.93 and 0.92, respectively. Indirect comparison no significant difference in the diagnostic performanceof DCE-MRI and DWI.

Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR

Background

Pancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.

Materials and Methods

Totally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.

Results

No significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433-0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.

Conclusions

The maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.

Magnetic resonance (MR) for mural nodule detection studying Intraductal papillary mucinous neoplasms (IPMN) of pancreas: Imaging-pathologic correlation

PURPOSE

Magnetic Resonance (MR) is recommended to diagnose Intraductal Papillary Mucinous Neoplasms (IPMN) and in the follow-up of borderline lesions. The purpose of this work is to evaluate the diagnostic accuracy of dynamic MR with Diffusion Weighted Imaging (DWI) in the identification of mural nodules of pancreatic IPMN by using pathological analysis as gold standard.

MATERIALS AND METHODS

Ninety-one preoperative MR with histopathological diagnosis of IPMN were reviewed by two radiologists. Presence, number and size of mural nodule, signal intensity of the nodule on T1-weighted imaging (T1-WI) after contrast medium administration and on DWI. Inter-observer agreement was evaluated.

RESULTS

Significant correlation (p < 0.0001) were found for presence of nodules > 5 mm on MR and pathological specimen, size and number of mural nodules evaluated on pathological review and degree of dysplasia, size and number of mural nodules evaluated on MR and tumoral dysplasia, presence of nodule > 5 mm with enhancement after contrast medium administration and hyperintensity on DWI and degree of dysplasia. Interobserver agreement was moderate for the presence of mural nodule (K = 0.56), for the presence of high signal intensity on DWI (K = 0.57) and enhancement of mural nodule (K = 0.58). Apparent Diffusion Coefficient (ADC) map histogram analysis showed a correlation between Entropy of the entire cystic lesion and the degree of dysplasia (p < 0.034).

CONCLUSIONS

MR with dynamic and DWI sequences was an accurate method for the identification of ≥ 5 mm solid nodules of the IPMNs and correlate with the lesion malignancy. Entropy, calculated from the histogram analysis of the IPMN ADC map, correlated with the lesion dysplasia.

French ccAFU guidelines – Update 2018–2020: Bladder cancer

Objective

To propose updated French guidelines for non-muscle invasive (NMIBC) and muscle-invasive (MIBC) bladder cancers.

Methods

A Medline search was achieved between 2015 and 2018, as regards diagnosis, options of treatment and follow-up of bladder cancer, to evaluate different references with levels of evidence.

Results

Diagnosis of NMIBC (Ta, T1, CIS) is based on a complete deep resection of the tumor. The use of fluorescence and a second-look indication are essential to improve initial diagnosis. Risks of both recurrence and progression can be estimated using the EORTC score. A stratification of patients into low, intermediate and high risk groups is pivotal for recommending adjuvant treatment: instillation of chemotherapy (immediate post-operative, standard schedule) or intravesical BCG (standard schedule and maintenance). Cystectomy is recommended in BCG-refractory patients. Extension evaluation of MIBC is based on contrast-enhanced pelvic-abdominal and thoracic CT-scan. Multiparametric MRI can be an alternative. Cystectomy associated with extended lymph nodes dissection is considered the gold standard for non-metastatic MIBC. It should be preceded by cisplatin-based neoadjuvant chemotherapy in eligible patients. An orthotopic bladder substitution should be proposed to both male and female patients with no contraindication and in cases of negative frozen urethral samples; otherwise transileal ureterostomy is recommended as urinary diversion. All patients should be included in an Early Recovery After Surgery (ERAS) protocol. For metastatic MIBC, first-line chemotherapy using platin is recommended (GC or MVAC), when performans status (PS < 1) and renal function (creatinine clearance > 60 mL/min) allow it (only in 50 % of cases). In second line treatment, immunotherapy with pembrolizumab demonstrated a significant improvement in overall survival.

Conclusion

These updated French guidelines will contribute to increase the level of urological care for the diagnosis and treatment for NMIBC and MIBC.

Combined Diagnosis of Whole-Lesion Histogram Analysis of T1- and T2-Weighted Imaging for Differentiating Adrenal Adenoma and Pheochromocytoma: A Support Vector Machine-Based Study

PURPOSE

This study aimed to determine whether a combined diagnosis of whole-lesion histogram analysis of T1- and T2-weighted imaging based on support vector machine (SVM) can distinguish pheochromocytoma from adrenal adenoma.

METHODS

A pathology database was retrospectively appraised over a period of 7 years and we obtained 40 histopathologically proven adrenal adenomas and 20 pheochromocytomas with magnetic resonance images. The T1-weighted imaging (T₁WI, including both in phase and opposed phase) and T2-weighted imaging (T₂WI) images of each patients were analyzed using Mazda software. Nine parameters were selected as indicators of comparison: variance, skewness, kurtosis, mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile. The parameters with differential-diagnosis significance were used to establish the combined diagnostic model of SVM.

RESULTS

Among the 9 parameters extracted using histogram analysis, the 1st percentile, 10th percentile, and 50th percentile of T₁WI (in phase) and the skewness of T₂WI and almost all parameters of T₁WI (opposed phase), except variance and 99th percentile, showed statistical significance between groups. Among the above parameters, the area under the curve (AUC) of 10th percentile of T₁WI (opposed phase) was the largest with the value of 0.909 (100.0% sensitivity and 80.0% specificity). After the analysis of combined diagnosis was performed, the AUC of SVM model in testing set showed the value of 0.917 (85.0% accuracy).

CONCLUSIONS

Whole-lesion histogram analysis of T₁WI and T₂WI may help differentiate adrenal adenomas from pheochromocytomas. Furthermore, the combined diagnosis of T₁WI and T₂WI histogram based on SVM was more effective than most of individual histogram parameters.

Differentiation between nasopharyngeal carcinoma and lymphoma at the primary site using whole-tumor histogram analysis of apparent diffusion coefficient maps

INTRODUCTION

To determine the value of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating nasopharyngeal carcinoma (NPC) from lymphoma (NPL) at the primary site METHOD AND MATERIALS: One hundred forty-seven patients with nasopharyngeal tumors (89 NPCs and 38 NPLs) who had undergone magnetic resonance imaging (MRI) and diffusion-weighted imaging were retrospectively analyzed. ADC histogram-derived parameters were compared between the NPC and NPL groups by using the Mann-Whitney U test. Receiver operating characteristic (ROC) curves of the histogram parameters were plotted for diagnostic accuracy. Sensitivity and specificity were calculated for each histogram parameter.

RESULTS

In whole-tumor histogram analysis, the mean, median, and 10th and 25th percentiles of ADC were all significantly higher in NPC than NPL (P = 0.045, P = 0.035, P = 0.005, and P = 0.016, respectively). Uniformity was significantly higher in NPC than NPL (P = 0.001). Skewness was significantly lower in NPC than NPL (P = 0.039). For the conventional ROI-based method, ADC_mean values were significantly higher in NPC than in NPL (P = 0.009). The ROC curve analysis showed that uniformity yielded the largest area under the curve (AUC = 0.768) for differentiating NPC from NPL among all ADC metrics, followed by 10th percentiles of ADC (AUC = 0.725); sensitivity and specificity were 76.5% and 71.4%, respectively.

CONCLUSION

Whole-tumor histogram analysis of ADC maps could be helpful for differentiating NPC from NPL.

Rectal Cancer Invasiveness: Whole-Lesion Diffusion-Weighted Imaging (DWI) Histogram Analysis by Comparison of Reduced Field-of-View and Conventional DWI Techniques

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) for discriminating between T stages of rectal carcinoma by comparison of reduced field-of-view (FOV) and conventional DWI techniques. 102 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative MR scan at 3 T, including reduced and full FOV DWI sequences. Histogram parameters from two DWI methods were calculated and correlated with histological T stage of rectal cancer. The diagnostic performance of individual parameter for differentiating stage pT1-2 and pT3-4 tumors from both DWI techniques was assessed by receiver operating characteristic curve analysis. There were significant differences for the parameters of ADCmean, 50th, 75th, 90th, 95th percentiles, skewness and kurtosis of both DWI sequences in patients with pT1-2 as compared to those with pT3-4 tumors (P < 0.05), in addition to parameters including ADCmin (P = 0.015) and 25th percentile (P = 0.006) from rFOV DWI. Correlations were noted between T staging and above histogram parameters from rFOV DWI (r: −0.741–0.682) and fFOV DWI (r: −0.449–0.449), besides parameters of ADCmin (0.370) and 25th percentile (−0.425) from rFOV DWI. The AUCs of 75th and 90th percentiles from rFOV DWI were significantly higher than that from fFOV DWI (P = 0.0410 and P = 0.0208). The whole-lesion histogram analysis based on rFOV DWI was overall more advantageous than the one based on fFOV DWI in differentiating T staging of rectal cancer and the 90th percentile ADC from rFOV DWI was the value with the highest AUC (0.932).

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for assessing orbital lymphoproliferative disorders (OLPDs). However, only the mean values of quantitative parameters were obtained in previous studies and tumor heterogeneity was ignored.

PURPOSE

To assess the value of DCE-MRI derived histogram parameters in differentiating malignant from benign OLPDs.

MATERIAL AND METHODS

Forty-eight OLPDs patients (25 malignant and 23 benign) who had undergone DCE-MRI for pre-treatment evaluation were retrospectively included. Histogram parameters of K^trans, k_ep, and v_e were calculated and compared between two groups using the independent sample's t-test. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of malignant OLPDs.

RESULTS

Tenth k_ep, mean k_ep, median k_ep, and 90th k_ep were significantly higher in the malignant OLPD group than in the benign OLPD group. Tenth v_e was significantly lower in the malignant OLPD group than in the benign OLPD group. Ninetieth k_ep was the only independent predictor of malignant OLPDs ( P = 0.019), with an area under ROC curve of 0.828, a sensitivity of 92.00%, and a specificity of 78.26% at a cut-off value of 1.057 min^-1.

CONCLUSION

Histogram analysis of DCE-MRI derived parameters may help to differentiate malignant from benign OLPDs. The 90th k_ep hold the potential as an independent predictor for malignant OLPDs.

Integrated analysis of 18F-FDG PET/CT improves preoperative lymph node staging for patients with invasive bladder cancer

OBJECTIVES

Preoperative ¹⁸F-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) is controversial to assess lymph node (LN) staging in patients with invasive bladder cancer. We proposed to use the maximum standardized uptake value (SUV_max) associated with axial-based LN size to improve the detection of regional LN metastasis.

METHODS

From May 2015 to May 2017, we prospectively included patients with urothelial bladder cancer who underwent radical cystectomy with extended pelvic LN dissection. All patients underwent preoperative ¹⁸F-FDG PET/CT staging before surgery. The gold standard comparator was the pathological examination of resected LNs. The data were reported on a regional per area- and patient-based model according to SUV_max values and axial-based LN size criteria.

RESULTS

In total, 1012 LNs were identified in 61 patients with clinically localized invasive bladder cancer who underwent radical cystectomy and extended pelvic LN dissection. Loco-regional involvement of 24 LN areas was confirmed in 17 patients. In per area analysis, diagnostic accuracy of PET/CT and CT alone were respectively 84% and 78% (p = 0.039). On patient-based analysis, combined PET/CT correctly classified pelvic LN status in 5/61 (+ 8%) additional patients using optimal thresholds compared to CT alone, with accuracies of 82% and 74%, respectively (p = 0.13).

CONCLUSION

Combining SUV_max and axial-based LN size criteria using ¹⁸F-FDG PET/CT improved the diagnostic accuracy for preoperative LN staging in patients with invasive bladder cancer, in per area analysis.

KEY POINTS

• Combining metabolical and morphological features using¹⁸F-FDG PET/CT improves the detection of malignant lymph node in patients with bladder cancer. • ¹⁸ F-FDG PET/CT may help for initial staging of patients with muscle invasive bladder cancer.

RETRACTED: Recommandations françaises du Comité de Cancérologie de l’AFU — Actualisation 2018—2020 : tumeurs de la vessie French ccAFU guidelines — Update 2018—2020: Bladder cancer

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). Cet article est retiré de la publication à la demande des auteurs car ils ont apporté des modifications significatives sur des points scientifiques après la publication de la première version des recommandations. Le nouvel article est disponible à cette adresse: doi:10.1016/j.purol.2019.01.006. C’est cette nouvelle version qui doit être utilisée pour citer l’article. This article has been retracted at the request of the authors, as it is not based on the definitive version of the text because some scientific data has been corrected since the first issue was published. The replacement has been published at the doi:10.1016/j.purol.2019.01.006. That newer version of the text should be used when citing the article.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

OBJECTIVES

To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness.

METHODS

Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter.

RESULTS

ADC_entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC_kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05).

CONCLUSIONS

Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC_entropy and ADC_kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour.

KEY POINTS

• Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Quantitative Assessment of Heterogeneity in Bladder Tumor MRI Diffusivity: Can Response be Predicted Prior to Neoadjuvant Chemotherapy?

Background:

It is a critical unmet need to predict chemosensitivity in muscle-invasive bladder cancer patients who receive neoadjuvant chemotherapy (NAC). Quantification of tumor heterogeneity has been shown to be useful in the assessment of therapeutic response. Apparent diffusion coefficient (ADC) is derived from diffusion weighted MRI (DWI) to quantify the water diffusivity which characterizes micro-cellularity in tumor tissues.

Objective:

The aim of this study is to assess if a quantitative measurement of ADC heterogeneity in bladder tumors can be a predictor of therapeutic response to NAC.

Materials and Methods:

Twenty patients with pT2 bladder cancer have been included in this study. Patient MRI was performed on a 3T system with DWI prior to NAC. Regions of interest (ROIs) were placed over the whole tumor volume on ADC maps to acquire a data matrix of voxel-wise ADC values for each patient. We performed histogram analysis on each ADC data matrix to calculate uniformity (U) and entropy (E). These quantities were subsequently correlated with the patient’s response to chemotherapy. Statistical significance was found with P < 0.05.

Results:

Fifteen patients were categorized as responders, and five as non-responders. The data showed that tumors of responders were significantly higher in U (P = 0.01) and lower in E (P < 0.01) than non-responders. This finding indicates that resistant tumors were more heterogeneous in their spatial distribution of ADC values. While this difference in ADC heterogeneity was not always visually recognizable, it could be quantified by the data analytics.

Conclusions:

This study demonstrates that the quantitative readout of tumor heterogeneity in micro-cellularity is associated with the patient’s defined response to chemotherapy. Quantification of tumor ADC heterogeneity may provide useful information to enable the prediction of chemotherapeutic response prior to the treatment to improve patient outcomes.