Investigation of volumetric apparent diffusion coefficient histogram analysis for assessing complete response and clinical outcomes following pre-operative chemoradiation treatment for rectal carcinoma

Improved effects of the b-value for 2000 sec/mm2 DWI on an accurate qualitative and quantitative assessment of rectal cancer

BACKGROUND AND STUDY OBJECTIVES

A higher b-value Diffusion-weighted imaging (DWI) would improve the contrast between cancerous and noncancerous tissue. Apparent diffusion coefficient (ADC)-histogram analysis is a method that can provide statistical data and quantitative information on tumor heterogeneity. This study aimed to compare two high b-values (1000 and 2000 sec/mm2) DWI in tumor detection and diagnostic performance in identifying early-stage tumor rectal cancer.

PATIENTS AND METHODS

This blinded and blinded retrospective study involved 56 patients with rectal cancer and 45 patients. Two radiologists evaluated the qualitative detection parameters and quantitative parameters of the ADC evaluated histogram and compared them between two DWI sequences (b-value for 1000 sec/mm2 and 2000 sec/mm2). The characteristic curves were used to assess diagnostic administration for the ADC histogram in discriminating early-stage tumors.

RESULTS

The b-value for 2000 sec/mm2 DWI significantly improved AUCs, sensitivity, specificity, and precision and decreased false-positive rate for detection compared to the b-value for 1000 sec/mm2 (p < 0.05). The mean and fifth percentile ADC value for stage I using the b-value for 1000 sec/mm2 DWI was significantly higher than stage ≥ II (p = 0.036II and 0.016 respectively), as the well as fifth, 10th, mean ADC of the fifth, 10th, and 25th ADC percentile at b-value for 2000 sec/mm2 (p = 0.031, 0.014, 0.035 and 0.025 respectively). The AUCs of the fifth percentile ADC at b-value for 2000 sec/mm2 DWI in both readers in differentiating the stage Ⅰ tumor were the highest (0.732 and 0.751).

CONCLUSION

The b-value for 2000 sec/mm2 DWI could improve the accurate detection of rectal cancer. The fifth percentile ADC at b-value for 2000 sec/mm2 sec/mm2 DWI was more useful for discriminating early stage than the b-value for 1000 sec/mm2 DWI.

Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

BACKGROUND

The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity.

RESULTS

Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation.

CONCLUSIONS

In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Simple Summary

Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

PURPOSE

Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors.

METHODS

A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score.

RESULTS

The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice.

CONCLUSION

Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends

Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases

OBJECTIVES

This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC).

METHODS

A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis.

RESULTS

There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse.

CONCLUSIONS

The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases.

KEY POINTS

• ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review

Introduction: The standard of care for locoregionally advanced rectal cancer is neoadjuvant therapy (NA CRT) prior to surgery, of which 10-30% experience a complete pathologic response (pCR). There has been interest in using imaging features, also known as radiomics features, to predict pCR and potentially avoid surgery. This systematic review aims to describe the spectrum of MRI studies examining high-performing radiomic features that predict NA CRT response.Areas covered: This article reviews the use of pre-therapy MRI in predicting NA CRT response for patients with locoregionally advanced rectal cancer (T3/T4 and/or N1+). The primary outcome was to identify MRI radiomic studies; secondary outcomes included the power and the frequency of use of radiomic features.Expert opinion: Advanced models incorporating multiple radiomics categories appear to be the most promising. However, there is a need for standardization across studies with regards to; the definition of NA CRT response, imaging protocols, and radiomics features incorporated. Further studies are needed to validate current radiomics models and to fully ascertain the value of MRI radiomics in the response prediction for locoregionally advanced rectal cancer.

The role of readout-segmented echo-planar imaging-based diffusion-weighted imaging in evaluating tumor response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy

BACKGROUND

Accurate assessment of tumor response in rectal cancer could help individualize treatment.

PURPOSE

To evaluate the role of diffusion-weighted imaging (DWI) based on readout-segmented echo-planar imaging (rs-EPI) in assessing tumor response after neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Sixty-three patients with LARC who received neoadjuvant CRT and surgery were enrolled retrospectively. They all underwent pre- and post-CRT magnetic resonance examinations, including DWI using rs-EPI. According to pathological results, patients were grouped as pathological complete responder (pCR, n = 16) and non-pCR (n = 47). Visual assessment of residual tumor and whole-tumor histogram analysis of pre- and post-CRT apparent diffusion coefficient (ADC) map was performed by two radiologists; tumor volume on ADC map was also recorded.

RESULTS

Overall inter-observer agreement was good for histogram analysis (ICC = 0.543-0.999). Tumor volume reduction rate on ADC map showed no significant difference between the two groups (P = 0.468). Post-CRT mean, quantile values, and their percentage changes were higher in the pCR group (all P < 0.001). Post-CRT mean value had a good diagnostic power in selecting pCR (AUC = 0.855), with a cut-off value of 1.345 × 10^-3 mm²/s, yielding a sensitivity of 83%, specificity of 81.3%. Post-CRT 95% quantile value had the highest AUC (AUC = 0.868) among quantile values, and a higher specificity (87.5% vs. 81.3%) than mean value with comparable overall diagnostic performance (P = 0.563). Visual assessment showed a sensitivity of 85.1%, specificity of 68.8% in selecting pCR.

CONCLUSION

Quantitative ADC value of rs-EPI DWI could reliably evaluate tumor response in patients with LARC. Post-CRT 95% quantile ADC value could help mean value to more accurately identify pCR.

The Predictive Value of Pre-/Postneoadjuvant Chemoradiotherapy MRI Characteristics for Patient Outcomes in Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

This study aimed to investigate the predictive value of pre-/postneoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) characteristics for the long-term survival outcomes in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

We retrospectively evaluated pre- and post-nCRT MRI and clinicopathologic characteristics of LARC patients. The 3-year disease-free survival (DFS) was estimated using the Kaplan-Meier product-limit method. Associations between MRI variabilities and survival outcomes were assessed using Cox proportional hazards model.

RESULTS

In total, 171 LARC patients (112 men and 59 women) with a median age of 55 years (range, 27-82 years) treated with nCRT were evaluated. The median follow-up was 47.6 months, and the 3-, 4-, and 5-year DFS in the overall cohort was 76.6%, 74.5%, and 73.7%, respectively. MRI assessment of extramural venous invasion (mrEMVI) positivity was a significant independent adverse factor of long-term survival (hazard ratio [HR] = 2.589, 95% confidence interval [CI] = 1.398-4.794, p = 0.002) on multivariate analysis. Patients with positive mrEMVI had significantly lower 3-year DFS than those with negative mrEMVI (52.6 months vs 65.1 months; p = 0.003). Moreover, the tumor regression grade on MRI (mrTRG) also significantly correlated with survival outcomes in patients with LARC. Patients with partial response on post-nCRT MRI (mrPR) showed short DFS than those with complete response (mrCR; HR = 4.914, 95% CI = 1.176-20.533, p = 0.029). The 3-year DFS of mrCR and mrPR patients were 74.3 months and 58.9 months, respectively (p = 0.011).

CONCLUSION

The pre-/post-nCRT MRI characteristics may be used to long-term survival stratification in LARC patients. mrEMVI positivity was an independent adverse prognostic indicator for 3-year DFS. Further, mrTRG may also be a predictive factor for the prognosis of LARC patients. The pre-/post-nCRT MR imaging may offer more information for providing individualized treatment.

Volumetric Histogram Analysis of Apparent Diffusion Coefficient as a Biomarker to Predict Survival of Esophageal Cancer Patients

BACKGROUND

The purpose of this study was to investigate whether histogram analysis of an apparent diffusion coefficient (ADC) can serve as a prognostic biomarker for esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study enrolled 116 patients with ESCC who received curative surgery from 2006 to 2015 (including 70 patients who received neoadjuvant chemotherapy). Diffusion-weighted magnetic resonance imaging (DWI) was performed prior to treatment. The ADC maps were generated by DWIs at b = 0 and 1000 (s/mm2), and analyzed to obtain ADC histogram-derived parameters (mean ADC, kurtosis, and skewness) of the primary tumor. Associations of these parameters with pathological features were analyzed, and Cox regression and Kaplan-Meier analyses were performed to compare these parameters with recurrence-free survival (RFS) and disease-specific survival (DSS).

RESULTS

Kurtosis was significantly higher in tumors with lymphatic invasion (p = 0.005) with respect to the associations with pathological features. In univariate Cox regression analysis, tumor depth, lymph node status, mean ADC, and kurtosis were significantly correlated with RFS (p = 0.047, p < 0.001, p = 0.037, and p < 0.001, respectively), while lymph node status and kurtosis were also correlated with DSS (p = 0.002 and p = 0.017, respectively). Furthermore, multivariate analysis demonstrated that kurtosis was the independent prognostic factor for both RFS and DSS (p < 0.001 and p = 0.015, respectively). In Kaplan-Meier analysis, patients with higher kurtosis tumors (> 3.24) showed a significantly worse RFS and DFS (p < 0.001 and p = 0.006, respectively).

CONCLUSIONS

Histogram analysis of ADC may serve as a useful biomarker for ESCC, reflecting pathological features and prognosis.

Diffusion-weighted imaging in rectal cancer: current applications and future perspectives

This review summarizes current applications and clinical utility of diffusion-weighted imaging (DWI) for rectal cancer and in addition provides a brief overview of more recent developments (including intravoxel incoherent motion imaging, diffusion kurtosis imaging, and novel postprocessing tools) that are still in more early stages of research. More than 140 papers have been published in the last decade, during which period the use of DWI have slowly moved from mainly qualitative (visual) image interpretation to increasingly advanced methods of quantitative analysis. So far, the largest body of evidence exists for assessment of tumour response to neoadjuvant treatment. In this setting, particularly the benefit of DWI for visual assessment of residual tumour in post-radiation fibrosis has been established and is now increasingly adopted in clinics. Quantitative DWI analysis (mainly the apparent diffusion coefficient) has potential, both for response prediction as well as for tumour prognostication, but protocols require standardization and results need to be prospectively confirmed on larger scale. The role of DWI for further clinical tumour and nodal staging is less well-defined, although there could be a benefit for DWI to help detect lymph nodes. Novel methods of DWI analysis and post-processing are still being developed and optimized; the clinical potential of these tools remains to be established in the upcoming years.

Clinical and molecular diagnosis of pathologic complete response in rectal cancer: an update

INTRODUCTION

The standard of care for locally advanced rectal cancer includes neoadjuvant chemoradiation with subsequent total mesorectal excision. This approach has shown various degrees of response to neoadjuvant chemoradiation (ranging from complete response to further tumor growth), which have substantial prognostic and therapeutic implications. A total regression of the tumor is a predictor of superior oncologic outcomes compared with partial responders and non-responders. Further, this concept has opened the possibility of nonoperative strategies for complete responders and explains the widespread research interest in finding clinical, radiographic, pathologic, and biochemical parameters that allow for identification of these patients. Areas covered: The present review evaluates the most recent efforts in the literature to identify predictors of patients likely to achieve a complete response following neoadjuvant treatment for the management of rectal cancer. This includes clinical predictors of pathologic complete response such as tumor location, size, and stage, molecular predictors such as tumor biology and microRNA, serum biomarkers such as carcinoembryogenic antigen and nomograms. Expert commentary: There has been significant progress in our ability to predict pathological complete response. However, more high-quality research is still needed to use this concept to confidently dictate clinical management.

Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study

Functional imaging modalities like Diffusion-weighted imaging are increasingly used to predict tumor behavior like cellularity and vascularity in different tumors. Histogram analysis is an emergent imaging analysis, in which every voxel is used to obtain a histogram and therefore statistically information about tumors can be provided. The purpose of this study was to elucidate possible associations between ADC histogram parameters and several immunhistochemical features in rectal cancer. Overall, 11 patients with histologically proven rectal cancer were included into the study. There were 2 (18.18%) females and 9 males with a mean age of 67.1 years. KI 67-index, expression of p53, EGFR, VEGF, and Hif1-alpha were semiautomatically estimated. The tumors were divided into PD1-positive and PD1-negative lesions. ADC histogram analysis was performed as a whole lesion measurement using an in-house matlab application. Spearman's correlation analysis revealed a strong correlation between EGFR expression and ADCmax (p=0.72, P=0.02). None of the vascular parameters (VEGF, Hif1-alpha) correlated with ADC parameters. Kurtosis and skewness correlated inversely with p53 expression (p=-0.64, P=0.03 and p=-0.81, P=0.002, respectively). ADCmedian and ADCmode correlated with Ki67 (p=-0.62, P=0.04 and p=-0.65, P=0.03, respectively). PD1-positive tumors showed statistically significant lower ADCmax values in comparison to PD1-negative tumors, 1.93 ± 0.36 vs 2.32 ± 0.47×10^-3mm²/s, p=0.04. Several associations were identified between histogram parameter derived from ADC maps and EGFR, KI 67 and p53 expression in rectal cancer. Furthermore, ADCmax was different between PD1 positive and PD1 negative tumors indicating an important role of ADC parameters for possible future treatment prediction.

MR Imaging with Apparent Diffusion Coefficient Histogram Analysis: Evaluation of Locally Advanced Rectal Cancer after Chemotherapy and Radiation Therapy

Purpose To determine response to neoadjuvant chemotherapy and radiation therapy in patients with locally advanced rectal cancer (LARC) by using magnetic resonance (MR) apparent diffusion coefficient (ADC) histogram analysis. Materials and Methods Ninety-two patients with LARC underwent MR imaging with rectal barium before and after chemotherapy and radiation therapy (CRT). Rectal expansion with barium expanded the lumen, provided similar imaging geometry before and after CRT, and eliminated fecal matter, air, and residual fluid. T2-weighted images, the percentage change in ADC, and ADC histogram skewness and kurtosis were assessed. The histopathologic tumor regression grade (TRG) ranged from 1a (66%-99% residual tumor cells) to 3 (no residual cells). The Wilcoxon signed-rank test, the Spearman correlation test, multivariable linear regression, and one-way analysis of variance were used to determine post- and pretreatment differences and correlations between tumor size and ADC. Results Of the 92 patients, 16 (17.4%) had TRG 3, 27 (29.3%) had TRG 2b, 24 (26.1%) had TRG 2a, 14 (15.2%) had TRG 1b, and 11 (12%) had TRG 1a. Post-CRT skewness (regression coefficient = 10.9, P = .06) and percentage ADC change (regression coefficient = -0.18, P = .03) were associated with the percentage of residual tumor. Post-CRT skewness and percentage ADC change, respectively, showed negative and positive correlation with histopathologic TRG (post-CRT skewness: P = .024; percentage ADC change: P = .001). Conclusion In patients with LARC, post-CRT skewness of the ADC histogram and percentage change in ADC were useful for predicting a favorable response to neoadjuvant CRT. ^© RSNA, 2018 Online supplemental material is available for this article.