Dataset Information

Early Cervical Cancer: Predictive Relevance of Preoperative 3-Tesla Multiparametric Magnetic Resonance Imaging.

ABSTRACT:

Objective

We assess the predictive significance of preoperative 3-Tesla multiparametric MRI findings.

Methods

A total of 260 patients with FIGO IA2-IIA cervical cancer underwent primary surgical treatment between 2007 and 2016. Univariable and multivariable logistic regression analyses were used to assess the incremental prognostic significance.

Results

The clinical predictive factors associated with pT2b disease were MRI parametrial invasion (PMI) (adjusted odds ratio (AOR) 3.77, 95% confidence interval(CI) 1.62-8.79; P=0.02) and MRI uterine corpus invasion (UCI) (AOR 9.99, 95% CI 4.11-24.32; P<0.0001). In multivariable analysis, for underdiagnoses, histologically squamous carcinoma versus adenocarcinoma and adenosquamous carcinoma (AOR 2.07, 95% CI 1.06-4.07; P=0.034) and MRI tumor size (AOR 0.76, 95% CI 0.63-0.92; P=0.005) were significant predictors; for overdiagnoses, these results were MRI tumor size (AOR 1.51, 95% CI 1.06-2.16; P=0.023), MRI PMI (AOR 71.73, 95% CI 8.89-611.38; P<0.0001) and MRI UCI (AOR 0.19, 95% CI 0.01-1.01; P=0.051).

Conclusion

PMI and UCI on T2-weighted images through preoperative 3T MRI are useful coefficients for accurate prediction of the pT2b stage; however, careful surveillance is required. Therefore, preoperative decision-making for early cervical cancer patients based on MRI diagnosis should be considered carefully, particularly in the presence of factors that are known to increase the likelihood of misdiagnosis.

SUBMITTER: Roh HJ

PROVIDER: S-EPMC6092969 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Early Cervical Cancer: Predictive Relevance of Preoperative 3-Tesla Multiparametric Magnetic Resonance Imaging.

Roh Hyun Jin HJ Kim Kyung Bin KB Lee Jong Hwa JH Kim Hwa Jung HJ Kwon Yong-Soon YS Lee Sang Hun SH

International journal of surgical oncology 20180801

<h4>Objective</h4>We assess the predictive significance of preoperative 3-Tesla multiparametric MRI findings.<h4>Methods</h4>A total of 260 patients with FIGO IA2-IIA cervical cancer underwent primary surgical treatment between 2007 and 2016. Univariable and multivariable logistic regression analyses were used to assess the incremental prognostic significance.<h4>Results</h4>The clinical predictive factors associated with pT2b disease were MRI parametrial invasion (PMI) (adjusted odds ratio (AOR ...[more]

PMID: 30155294

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Project description:Take Home Message Positive surgical margin status, biochemical recurrence-free survival, and 1-yr postoperative 26-item Expanded Prostate Cancer Index Composite functional domain scores were similar regardless of whether computed tomography or multiparametric magnetic resonance imaging was used preoperatively to guide surgical planning for the treatment of localized prostate cancer. Background Advancements in imaging technology have been associated with changes to operative planning in treatment of localized prostate cancer. The impact of these changes on postoperative outcomes is understudied. Objective To compare oncologic and functional outcomes between men who had computed tomography (CT) and those who had multiparametric magnetic resonance imaging (mpMRI) prior to undergoing radical prostatectomy. Design, setting, and participants In this retrospective cohort study, we identified all men who underwent radical prostatectomy (n = 1259) for localized prostate cancer at our institution between 2009 and 2016. Of these, 917 underwent preoperative CT and 342 mpMRI. Outcome measurements and statistical analysis Biochemical recurrence-free survival, positive margin status, postoperative complications, and 1-yr postprostatectomy functional scores (using the 26-item Expanded Prostate Cancer Index Composite [EPIC-26] questionnaire) were compared between those who underwent preoperative CT and those who underwent mpMRI using propensity score weighted Cox proportional hazard regression, logistic regression, and linear regression models. Results and limitations Baseline and 1-yr follow-up EPIC-26 data were available for 449 (36%) and 685 (54%) patients, respectively. After propensity score weighting, no differences in EPIC-26 functional domains were observed between the imaging groups at 1-yr follow-up. Positive surgical margin rates (odds ratio 1.03, 95% confidence interval [CI] 0.77–1.38, p = 0.8) and biochemical recurrence-free survival (hazard ratio 1.21, 95% CI 0.84–1.74, p = 0.3) were not significantly different between groups. Early and late postoperative complications occurred in 219 and 113 cases, respectively, and were not different between imaging groups. Our study is limited by a potential selection bias from the lack of functional scores for some patients. Conclusions In this single-center study of men with localized prostate cancer undergoing radical prostatectomy, preoperative mpMRI had minimal impact on functional outcomes and oncologic control compared with conventional imaging. These findings challenge the assumptions that preoperative mpMRI improves operative planning and perioperative outcomes. Patient summary In this study, we assessed whether the type of prostate imaging performed prior to surgery for localized prostate cancer impacted outcomes. We found that urinary and sexual function, cancer control, and postoperative complications were similar regardless of whether magnetic resonance imaging or computed tomography was utilized prior to surgery.

Project description:PurposeThe clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.MethodsWe retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa.ResultsUnivariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.ConclusionOur multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.

Project description:Simple Summary Magnetic resonance imaging (MRI) has been considered a noninvasive prognostic biomarker in some cancers; however, the correlation with pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. The aim of our study was to identify quantitative MRI parameters associated with prognosis and recurrence patterns. In an analysis of data from the 136 patients ultimately included in this study, we found that the value of the pure diffusion coefficient D in intravoxel incoherent MRI is an independent risk factor for overall survival (OS) and recurrence-free survival (RFS), while a low value of D is significantly associated with a higher risk of local recurrence. All the patients have been operated on with histopathology for further evaluation. Based on the results of our research, we believe that it is possible in clinical practice to stratify patients based on quantitative MRI data in order to guide treatment strategies, reduce the risk of local tumor recurrence, and improve patients’ prognosis. Abstract Magnetic resonance imaging (MRI) has been shown to be associated with prognosis in some tumors; however, the correlation in pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. In this retrospective study, we ultimately included 136 patients and analyzed quantitative MRI parameters that are associated with prognosis and recurrence patterns in PDAC using survival analysis and competing risks models; all the patients have been operated on with histopathology and immunohistochemical staining for further evaluation. In intravoxel incoherent motion diffusion-weighted imaging (DWI), we found that pure-diffusion coefficient D value was an independent risk factor for overall survival (OS) (HR: 1.696, 95% CI: 1.003–2.869, p = 0.049) and recurrence-free survival (RFS) (HR: 2.066, 95% CI: 1.252–3.409, p = 0.005). A low D value (≤1.08 × 10−3 mm2/s) was significantly associated with a higher risk of local recurrence (SHR: 5.905, 95% CI: 2.107–16.458, p = 0.001). Subgroup analysis revealed that patients with high D and f values had significantly better outcomes with adjuvant chemotherapy. Distant recurrence patients in the high-D value group who received chemotherapy may significantly improve their OS and RFS. It was found that preoperative multiparametric quantitative MRI correlates with prognosis and recurrence patterns in PDAC. Diffusion coefficient D value can be used as a noninvasive biomarker for predicting prognosis and recurrence patterns in PDAC.

Project description:Background:Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. Purpose:To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. Materials and Methods:Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. Results:T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). Conclusion:Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

Project description:ObjectivesTo compare the predictive performance of different radiomics signatures from multiparametric magnetic resonance imaging (mpMRI), including four sequences when used individually or combined, and to establish and validate an optimal nomogram for predicting perineural invasion (PNI) in rectal cancer (RC) patients.MethodsOur retrospective study included 279 RC patients without preoperative antitumor therapy (194 in the training dataset and 85 in the test dataset) who underwent preoperative mpMRI scan between January 2017 and January 2021. Among them, 72 cases were PNI-positive. Then, clinical and radiological variables were collected, including carcinoembryonic antigen (CEA), radiological tumour stage (T1-4), lymph node stage (N0-2) and so on. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), apparent diffusion coefficient (ADC), and enhanced T1WI (T1CE) sequences. The clinical model was constructed by integrating the final selected clinical and radiological variables. The radiomics signatures included four single-sequence signatures and one fusion signature were built using the respective remaining optimized features. And the nomogram was constructed based on the independent predictors by using multivariable logistic regression. The area under curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance.ResultsUltimately, 20 radiomics features were retained from the four sequences-T1WI (n = 4), T2WI (n = 5), ADC (n = 5), and T1CE (n = 6)-to construct four single-sequence radiomics signatures and one fusion radiomics signature. The fusion radiomics signature performed better than four single-sequence radiomics signatures and clinical model (AUCs of 0.835 and 0.773 vs. 0.680-0.737 and 0.666-0.709 in the training and test datasets, respectively). The nomogram constructed by incorporating CEA, tumour stage and rad-score performed best, with AUCs of 0.869 and 0.864 in the training and test datasets, respectively. Delong test showed that the nomogram was significantly different from the clinical model and four single-sequence radiomics signatures (P < 0.05). Moreover, calibration curves demonstrated good agreement, and DCA highlighted benefits of the nomogram.ConclusionsThe comprehensive nomogram can preoperatively and noninvasively predict PNI status, provide a convenient and practical tool for treatment strategy, and help optimize individualized clinical decision-making in RC patients.

Dataset Information

Early Cervical Cancer: Predictive Relevance of Preoperative 3-Tesla Multiparametric Magnetic Resonance Imaging.

Objective

Methods

Results

Conclusion

Publications

Early Cervical Cancer: Predictive Relevance of Preoperative 3-Tesla Multiparametric Magnetic Resonance Imaging.

Similar Datasets

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