Assessment of the Early Predictive Power of Quantitative Magnetic Resonance Imaging Parameters During Neoadjuvant Chemotherapy for Uterine Cervical Cancer

Himoto, Yuki; Fujimoto, Koji; Kido, Aki; Matsumura, Noriomi; Baba, Tsukasa; Daido, Sayaka; Kiguchi, Kayo; Shitano, Fuki; Konishi, Ikuo; Togashi, Kaori

doi:10.1097/igc.0000000000000124

Cited by 17 publications

(10 citation statements)

References 23 publications

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“…[ 19 ] To the best of our knowledge, few reports have evaluated the predictive power of DWI for determining the NACT outcome for cervical cancer. [ 8 9 ] Based on its molecular diffusion and perfusion properties, IVIM represents a noninvasive method for predicting treatment responses before treatment and for monitoring responses during treatment. We collected a dataset that included 42 cases of cervical cancer and evaluated the predictive value of IVIM parameters in terms of the response to NACT.…”

Section: Discussionmentioning

confidence: 99%

“…Diffusion-weighted magnetic resonance imaging (DWI) is a proven quantitative biomarker for the therapeutic response to chemoradiotherapy (CRT) and NACT in cervical cancer. [ 8 9 10 ] Studies have demonstrated significantly different apparent diffusion coefficient (ADC) values between responders and nonresponders after therapy; therefore, DWI may be a good tool for evaluating treatment responses. However, ADCs obtained from DWI with a monoexponential model can be influenced not only by molecular diffusion but by microcirculation or blood perfusion; therefore, ADC values that include perfusion effects may limit the reliability of this tool.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Early Response of Advanced Cervical Cancer to Neoadjuvant Chemotherapy Using Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging

Wang

et al. 2016

Chinese Medical Journal

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Background:Diffusion-weighted imaging (DWI) with the intravoxel incoherent motion (IVIM) model has shown promising results for providing both diffusion and perfusion information in cervical cancer; however, its use to predict and monitor the efficacy of neoadjuvant chemotherapy (NACT) in cervical cancer is relatively rare. The study aimed to evaluate the use of DWI with IVIM and monoexponential models to predict and monitor the efficacy of NACT in cervical cancer.Methods:Forty-two patients with primary cervical cancer underwent magnetic resonance exams at 3 time points (pre-NACT, 3 weeks after the first NACT cycle, and 3 weeks after the second NACT cycle). The response to treatment was determined according to the response evaluation criteria in solid tumors 3 weeks after the second NACT treatment, and the subjects were classified as two groups: responders and nonresponders groups. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion-related pseudo-diffusion coefficient (D*), and perfusion fraction (f) values were determined. The differences in IVIM-derived variables and ADC between the different groups at the different time points were calculated using an independent samples t-test.Results:The D and ADC values were all significantly higher for the responders than for the nonresponders at all 3 time points, but no significant differences were observed in the D* and f values. An analysis of the receiver operating characteristic (ROC) curves indicated that a D value threshold <0.93 × 10−3 mm2/s and an ADC threshold <1.11 × 10−3 mm2/s could differentiate responders from nonresponders at pre-NACT time point, yielding area under the curve (AUC) of which were 0.771 and 0.806, respectively. The ROC indicated that the AUCs of D and ADC at the 3 weeks after the first NACT cycle and 3 weeks after the second NACT cycle were 0.823, 0.763, and 0.787, 0.794, respectively. The AUC values of D and ADC at these 3 time points were not significantly different (P = 0.641, 0.512, and 0.547, respectively).Conclusions:D and ADC values may be useful for predicting and monitoring the efficacy of NACT in cervical cancer. An IVIM model may be equal to monoexponential model in predicting and monitoring the efficacy of NACT in cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Early Response of Advanced Cervical Cancer to Neoadjuvant Chemotherapy Using Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging

Wang

et al. 2016

Chinese Medical Journal

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“…Some proposed genomic, proteomic predictive signature are limited by small sample size and high cost of examination (13,14). Several pioneer studies demonstrated quantitative parameters derived from pre-and post-treatment functional imaging of LACC patients were powerful markers to noninvasively predict early therapeutic response to NACT (15,16). These quantitative parameters allow for characterizing tumor biological process during NACT.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis

et al. 2020

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Objective: To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC).Patients and Methods: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients.Results: Radiomics signature containing pre-and post-contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively] and remain relatively stable across centers. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697-0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration.Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients.

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“…In terms of cost-effectiveness and clinical practicality, MRI-based biomarkers are generally superior to some proposed clinicopathologic, genomic, or proteomic predictors of the NACT response. In addition, comparing quantitative parameters extracted from pre- and post-treatment functional MRI has been demonstrated to improve response assessment by detecting invisible biological properties of cervical tumour [[15], [16], [17]]. However, most research methods are conducted on a post-treatment basis and yield variable diagnostic performances, thereby restricting the utility in the initial decision making regarding NACT.…”

Section: Introductionmentioning

confidence: 99%

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

et al. 2019

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Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p b .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used. Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

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Assessment of the Early Predictive Power of Quantitative Magnetic Resonance Imaging Parameters During Neoadjuvant Chemotherapy for Uterine Cervical Cancer

Abstract: Early TVRR, Ve, and the change of Ve could be useful predictors for the treatment effectiveness of NACT. These parameters could help to modify strategy in the early stage of NACT and to choose individualized treatment to avoid the delay of radical treatment, even when NACT is ineffective.

Cited by 17 publications

References 23 publications

Assessing the Early Response of Advanced Cervical Cancer to Neoadjuvant Chemotherapy Using Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging

Assessing the Early Response of Advanced Cervical Cancer to Neoadjuvant Chemotherapy Using Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

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