Quantitative Assessment of Rectal Cancer Response to Neoadjuvant Combined Chemotherapy and Radiation Therapy: Comparison of Three Methods of Positioning Region of Interest for ADC Measurements at Diffusion-weighted MR Imaging

Correlations between Apparent Diffusion Coefficient (ADC) and Prognosis in Patients with Locally Advanced Rectal Cancer

BACKGROUND

the aim of this study is to assess the performance of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values in predicting the response to neoadjuvant chemoradiation therapy (CRT) and outcome in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

ninety-four patients with magnetic resonance imaging (MRI) pre- and post-neoadjuvant treatment were retrospectively enrolled. Three regions of interest (ROIs) were manually drawn on three different slices of the tumor for every DWI sequence. ROIs were positioned to include only high signal areas and avoid artifacts or necrotic areas. ROIs were automatically copied onto the corresponding ADC maps and the system derived three different ADC values, distinguishing between mean, maximum, and minimum values, and the standard deviation (SD). Only mean ADC values were considered. After surgical intervention, pTNM and the Mandard tumor regression grade (TRG) were obtained. Patients with a TRG of 1-2 were classified as responders, while patients with a TRG from 3 to 5 were classified as non-responders.

RESULTS

no correlation was found between pre-ADC values and TRG classes, while post-ADC and ΔADC values showed a significant correlation with TRG classes (r = -0.285, p = 0.002 and r = -0.290, p = 0.019, respectively). Post-ADC values were statistically different between responders and non-responders (p = 0.019). When considering the relation between overall survival (OS) and ADC values, pre-ADC showed a negative correlation with OS (r = -0.381, p = 0.001), while a positive correlation was found between ΔADC values and OS (r = 0.323, p = 0.013). According to ΔADC values, the mean OS time between responders and non-responders showed a significant difference (p = 0.030). A statistical difference was found between TRG classes and OS (p = 0.038) and by dividing patients in responders and non-responders (p = 0.019).

CONCLUSIONS

the pre-ADC and ΔADC values could be used as useful predictors for patient prognosis, thus helping the treatment planning. On the other hand, the post-ADC values, thanks to their relationship with the TRG classes, could be the ideal tool to predict the histopathological response and plan a conservative approach to the treatment of rectal cancer.

Significance of Normalized Apparent Diffusion Coefficient in the Vesical Imaging-Reporting and Data System for Diagnosing Muscle-Invasive Bladder Cancer

BACKGROUND

Vesical Imaging-Reporting and Data System (VI-RADS) has been developed for assessing bladder cancer from multiparametric (mp) MRI but its performance in diagnosing muscle-invasive bladder cancer (MIBC) is suboptimal.

PURPOSE

To investigate associations between normalized apparent diffusion coefficient (NADC) and clinicopathological characteristics and to determine whether the inclusion of NADC can improve the performance of VI-RADS in diagnosing MIBC.

STUDY TYPE

Retrospective.

POPULATION

Two hundred seventy-five patients with pathologically confirmed bladder cancer (101 MIBC and 174 non-MIBC [NMIBC]) underwent preoperative mpMRI (233 male, 42 female).

FIELD STRENGTH/SEQUENCE

3-T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (free-breathing spin-echo), and dynamic contrast-enhanced imaging (gradient-echo).

ASSESSMENT

NADC was the mean ADC of tumor divided by that of the iliopsoas muscles in trans caput femoris plane. Associations between NADC and clinicopathological characteristics were evaluated. Models were established for differentiating MIBC and NMIBC: VI-RADS model; VN model (VI-RADS and NADC), Images model (significant variables from imaging associated with MIBC), LN model (Images model without NADC), and Full model (all significant variables associated with MIBC).

STATISTICAL TESTS

Variables for model development were based on logistic regression. Models were evaluated by receiver operating characteristic (ROC) curve. Comparison of the area under the curves (AUCs) for the models used DeLong's test. A P value <0.05 was considered statistically significant.

RESULTS

NADC was significantly lower in lesions with diameter ≥ 3 cm, MIBC, histological high grade, lymph node metastasis, and lymphovascular invasion. Compared with VI-RADS model, the AUCs for VN model (VI-RADS score and NADC), Images model (VI-RADS score, NADC and tumor size) and Full model (VI-RADS score, NADC, tumor size and histological grade) were significantly higher. No significant differences were observed between the AUCs for VN model and Images model (P = 0.051).

DATA CONCLUSION

NADC reflects information about the aggressiveness of bladder cancer. Combining VI-RADS with NADC can improve performance in diagnosing MIBC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy

OBJECTIVES

This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC).

MATERIALS AND METHODS

A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods.

RESULTS

The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients.

CONCLUSION

The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT.

CLINICAL RELEVANCE STATEMENT

The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment.

KEY POINTS

• The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection

OBJECTIVE

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients.

METHODS

Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis.

RESULTS

The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts.

CONCLUSION

ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy.

CLINICAL RELEVANCE STATEMENT

The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients?

Background

Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT.

Methods

This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test.

Results

A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively.

Conclusions

The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.

Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy

BACKGROUND

Computed diffusion-weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed.

PURPOSE

To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT.

STUDY TYPE

Retrospective.

POPULATION

Eighty-seven consecutive patients.

FIELD STRENGTH/SEQUENCE

3.0 T/DWI.

ASSESSMENT

All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000 ) and another with b = 0 and 700 s/mm2 on a 3.0-T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000 ). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000 .

STATISTICAL TESTS

Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used.

RESULTS

The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0-Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists.

DATA CONCLUSION

Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer

RATIONALE AND OBJECTIVES

To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters.

MATERIALS AND METHODS

Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed.

RESULTS

The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model.

CONCLUSION

Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.

A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Objective

Radiomics based on magnetic resonance imaging (MRI) shows potential for prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC); however, thorough comparison between radiomics and traditional models is deficient. We aimed to construct multiple-time-scale (pretreatment, posttreatment, and combined) radiomic models to predict pathological complete response (pCR) and compare their utility to those of traditional clinical models.

Methods

In this research, 165 LARC patients undergoing nCRT followed by surgery were enrolled retrospectively, which were divided into training and testing sets in the ratio of 7:3. Morphological features on pre- and posttreatment MRI, coupled with clinical data, were evaluated by univariable and multivariable logistic regression analysis for constructing clinical models. Radiomic parameters were derived from pre- and posttreatment T2- and diffusion-weighted images to develop the radiomic signatures. The clinical-radiomics models were then generated. All the models were developed in the training set and then tested in the testing set, the performance of which was assessed using the area under the receiver operating characteristic curve (AUC). Radiomic models were compared with the clinical models with the DeLong test.

Results

One hundred and sixty-five patients (median age, 55 years; age interquartile range, 47-62 years; 116 males) were enrolled in the study. The pretreatment maximum tumor length, posttreatment maximum tumor length, and magnetic resonance tumor regression grade were selected as independent predictors for pCR in the clinical models. In the testing set, the pre- and posttreatment and combined clinical models generated AUCs of 0.625, 0.842, and 0.842 for predicting pCR, respectively. The MRI-based radiomic models performed reasonably well in predicting pCR, but neither the pure radiomic signatures (AUCs, 0.734, 0.817, and 0.801 for the pre- and posttreatment and combined radiomic signatures, respectively) nor the clinical-radiomics models (AUCs, 0.734, 0.860, and 0.801 for the pre- and posttreatment and combined clinical-radiomics models, respectively) showed significant added value compared with the clinical models (all P > 0.05).

Conclusion

The MRI-based radiomic models exhibited no definite added value compared with the clinical models for predicting pCR in LARC. Radiomic models can serve as ancillary tools for tailoring adequate treatment strategies.

MRI restaging of rectal cancer: The RAC (Response-Anal canal-CRM) analysis joint consensus guidelines of the GRERCAR and GRECCAR groups

PURPOSE

To develop guidelines by international experts to standardize data acquisition, image interpretation, and reporting in rectal cancer restaging with magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Evidence-based data and experts' opinions were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts provided recommendations for reporting template and protocol for data acquisition were collected; responses were analysed and classified as "RECOMMENDED" versus "NOT RECOMMENDED" (if ≥ 80% consensus among experts) or uncertain (if < 80% consensus among experts).

RESULTS

Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed.

CONCLUSION

These consensus recommendations should be used as a guide for rectal cancer restaging with MRI.

Feasibility of high-resolution readout-segmented echo-planar imaging with simultaneous multislice imaging in assessing rectal cancer

PURPOSE

To investigate the feasibility of high-resolution readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) imaging to predict well-differentiated rectal cancer.Kindly check and confirm whether the Author Name 'Hongyun Huang ' is correctly identified.confirm METHODS: A total of eighty-three patients with nonmucinous rectal adenocarcinoma received both prototype SMS high-spatial-resolution and conventional rs-EPI sequences. Image quality was subjectively assessed by two experienced radiologists using a 4-point Likert scale (1 = poor, 4 = excellent). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of the lesion were measured by two experienced radiologists in the objective assessment. Paired t tests or Mann‒Whitney U tests were used to compare the two groups. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to determine the predictive value of the ADCs in discriminating well-differentiated rectal cancer in the two groups. A two-sided p value < 0.05 represented statistical significance.Please check and confirm if the authors and affiliation details have been correctly identified. Amend if necessary.confirm RESULTS: In the subjective assessment, high-resolution rs-EPI had better image quality than conventional rs-EPI (p < 0.001). High-resolution rs-EPI also had a significantly higher SNR and CNR (p < 0.001). The T stage of rectal cancer was inversely correlated with the ADCs measured on high-resolution rs-EPI (r = -0.622, p < 0.001) and rs-EPI (r = -0.567, p < 0.001). The AUC of high-resolution rs-EPI in predicting well-differentiated rectal cancer was 0.768.

CONCLUSION

High-resolution rs-EPI with SMS imaging provided significantly higher image quality, SNRs, and CNRs and more stable ADC measurements than conventional rs-EPI. Additionally, the pretreatment ADC on high-resolution rs-EPI could discriminate well-differentiated rectal cancer.

A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors

OBJECTIVES

This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy.

METHODS

We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm² and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm². rFOV-syDWIs with b values of 1200 (rFOV-syDWI_b=1200) and 1500 (rFOV-syDWI_b=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene's test and Shapiro-Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann-Whitney U test.

RESULTS

A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWI_b=1200/1500 compared to 13b-protocol rFOV-sDWI_b=1200/1500 (p < 0.05). AUC of 5b-protocol syADC_{mean,b=1200/1500} and syADC_{minimum,b=1200/1500} was equal or higher than that of 13b-protocol sADC_{mean,b=1200/1500} and sADC_{minimum,b=1200/1500}.

CONCLUSIONS

rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.

Comparison of the diagnostic performance of changes in signal intensity and volume from multiparametric MRI for assessing response of rectal cancer to neoadjuvant chemoradiotherapy

AIM

To evaluate the change in signal intensity (SI) and volume (V) from multiparametric magnetic resonance imaging (MRI) for assessing the response of locally advanced rectal cancer (LARC) to chemoradiotherapy (CRT).

MATERIALS AND METHODS

Eight-two LARC patients who underwent pre- and post-CRT T2-weighted (T2W), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) MRI were retrospectively analyzed. The change of volume (%△V) and relative SI ratio (%△SIR) from each sequence were determined. All LARCs were confirmed pathologically and classified as tumor regression grade (TRG) -0, 1, 2,or 3. Descriptive statistics and receiver operating characteristic (ROC) analysis, with calculation of area under the curve (AUC), were used to compare the diagnostic performances.

RESULTS

Sixteen patients had TRG-0, 15 had TRG-1, 35 had TRG-2, and 16 had TRG-3. Except for ADC-%△SIR, the remaining %△V and %△SIR values on MR sequences had significant differences among the four groups. The %△V and %△SIR (alone or together) did not distinguish TRG-1 from TRG-2, nor TRG-2 from TRG-3; however, differences between other TRGs were identified by %△V and %△SIR. The combined use of ADC-%△V and T2W-%△SIR provided the best diagnostic performance in distinguishing of TRG-0 from TRG-2 (AUC: 0.954) and from TRG-3 (AUC: 1.000).

CONCLUSIONS

Preoperative MRI of LARC patients after CRT has high diagnostic value for determination TRG, and may therefore improve the selection of patients most suitable for surgery.

Apparent diffusion coefficient derived from diffusion-weighted imaging to differentiate between tumor, tumor-adjacent and tumor-distant tissues in resectable rectal adenocarcinoma

PURPOSE

To evaluate feasibility of apparent diffusion coefficient (ADC) at different b-values to differentiate between tumor, tumor-adjacent and tumor-distant tissues in rectal adenocarcinoma (RA).

MATERIALS AND METHODS

Seventy consecutive patients with RA undergoing preoperative diffusion-weighted imaging were retrospectively enrolled. ADCs of tumor, proximal tumor-adjacent tissue (PTA) and tumor-distant tissue (PTD), and distal tumor-adjacent tissue (DTA) and tumor-distant tissue (DTD) were calculated with b-values of 0 and 800 sec/mm2, 0 and 1000 sec/mm2, 0 and 1500 sec/mm2, and multiple b-values of 0, 50, 100, 800, 1000 and 1500 sec/mm2. Statistical analysis was performed to determine feasibility of ADC to differentiate between pairwise tissues.

RESULTS

Mean ADC of tumor was lower than those of PTA, PTD, DTA and DTD; and mean ADCs of PTA and DTA were lower than those of PTD and DTD at all b-values, respectively (all P-values < 0.001). ADC cut-offs of 1.089 × 10-3 mm2/sec (b = 0, 1000 sec/mm2) or 1.215 × 10-3 mm2/sec (b = 0, 800 sec/mm2), and 1.142 × 10-3 mm2/sec (b = 0, 1000 sec/mm2) or 0.995 × 10-3 mm2/sec (b = 0, 1500 sec/mm2) achieved excellent performance in differentiating tumor from PTA or PTD, and tumor from DTA or DTD (area under receiver operating characteristic curves [AUCs]: 0.813 or 0.952, and 0.970 or 0.996), respectively. ADC cut-offs of 1.625 × 10-3 mm2/sec (b = 0, 800 sec/mm2), and 1.165 × 10-3 mm2/sec (b = 0, 1500 sec/mm2) could differentiate PTA from PTD, and DTA from DTD (AUCs: 0.709 and 0.673), respectively.

CONCLUSION

ADC can help differentiate between tumor, tumor-adjacent and tumor-distant tissues in RA.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions

BACKGROUND

To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.

METHODS

One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.

RESULTS

Stage IA EC showed lower ADC_10th, ADC_90th, ADC_min, ADC_max, ADC_mean, ADC_median, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADC_median yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10^-3 mm²/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC_10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADC_min and ADC_10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).

CONCLUSIONS

Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.

Quantitative analysis of diffusion weighted imaging in rectal cancer during radiotherapy using a magnetic resonance imaging integrated linear accelerator

Background and purpose

Magnetic resonance imaging integrated linear accelerator (MR-Linac) platforms enable acquisition of diffusion weighted imaging (DWI) during treatment providing potential information about treatment response. Obtaining DWI on these platforms is technically different from diagnostic magnetic resonance imaging (MRI) scanners. The aim of this project was to determine feasibility of obtaining DWI and calculating apparent diffusion coefficient (ADC) parameters longitudinally in rectal cancer patients on the MR-Linac.

Materials and methods

Nine patients undergoing treatment on MR-Linac had DWI acquired using b-values 0, 30, 150, 500 s/mm2. Gross tumour volume (GTV) and normal tissue was delineated on DWI throughout treatment and median ADC was calculated using an in-house tool (pyOsirix ®).

Results

Seven out of nine patients were included in the analysis; all demonstrated downstaging at follow-up. A total of 63 out of 70 DWI were analysed (7 excluded due to poor image quality). An increasing trend of ADC median for GTV (1.15 × 10-3 mm2/s interquartile range (IQ): 1.05-1.17 vs 1.59 × 10-3 mm2/s IQ: 1.37 - 1.64; p = 0.0156), correlating to treatment response. In comparison ADC median for normal tissue remained the same between first and last fraction (1.61 × 10-3 mm2/s IQ: 1.56-1.71 vs 1.67 × 10-3 mm2/s IQ: 1.37-2.00; p = 0.9375).

Conclusions

DWI assessment in rectal cancer patients on MR-Linac is feasible. Initial results provide foundations for further studies to determine DWI use for treatment adaptation in rectal cancer.

MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

Background

To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).

Methods

Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.

Results

We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.

Conclusions

The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.

Comparison of Computed and Acquired DWI in the Assessment of Rectal Cancer: Image Quality and Preoperative Staging

Objective

The aim of the study was to evaluate the computed diffusion-weighted images (DWI) in image quality and diagnostic performance of rectal cancer by comparing with the acquired DWI.

Methods

A total of 103 consecutive patients with primary rectal cancer were enrolled in this study. All patients underwent two DWI sequences, namely, conventional acquisition with b = 0 and 1,000 s/mm2 (aDWIb1,000) and another with b = 0 and 700 s/mm2 on a 3.0T MR scanner (MAGNETOM Prisma; Siemens Healthcare, Germany). The images (b = 0 and 700 s/mm2) were used to compute the diffusion images with b value of 1,000 s/mm2 (cDWIb1,000). Qualitative and quantitative analysis of both computed and acquired DWI images was performed, namely, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal intensity ratio (SIR), and also diagnostic staging performance. Interclass correlation coefficients, weighted κ coefficient, Friedman test, Wilcoxon paired test, and McNemar or Fisher test were used for repeatability and comparison assessment.

Results

Compared with the aDWIb1,000 images, the cDWIb1,000 ones exhibited significant higher scores of subjective image quality (all P &lt;0.050). SNR, SIR, and CNR of the cDWIb1,000 images were superior to those of the aDWIb1,000 ones (P &lt;0.001). The overall diagnostic accuracy of computed images was higher than that of the aDWIb1,000 images in T stage (P &lt;0.001), with markedly better sensitivity and specificity in distinguishing T1–2 tumors from the T3–4 ones (P &lt;0.050).

Conclusion

cDWIb1,000 images from lower b values might be a useful alternative option and comparable to the acquired DWI, providing better image quality and diagnostic performance in preoperative rectal cancer staging.

Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer

OBJECTIVE

To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC).

METHODS

We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation.

RESULTS

The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05).

CONCLUSION

The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.

Diagnostic performance of MRI and endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy: a systematic review of the literature

BACKGROUND

The diagnostic performance of magnetic resonance imaging (MRI) modalities and/or endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy (nCRT) is unclear.

PURPOSE

To summarize existing evidence on the diagnostic performance of diffusion-weighted MRI, perfusion-weighted MRI, T2-weighted MR tumor regression grade, and/or endoscopy for assessing complete tumor response after nCRT.

MATERIAL AND METHODS

MEDLINE and Embase databases were searched. The PRISMA guidelines were followed. Sensitivity, specificity, negative predictive, and positive predictive values were retrieved from included studies.

RESULTS

In total, 81 studies were eligible for inclusion. Evidence suggests that combined use of MRI and endoscopy tends to improve the diagnostic performance compared to single imaging modality. The positive predictive value of a complete response varies substantially between studies. There is considerable heterogeneity between studies.

CONCLUSION

Combined re-staging tends to improve diagnostic performance compared to single imaging modality, but the vast majority of studies fail to offer true clinical value due to the study heterogeneity.

MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

RATIONALE AND OBJECTIVES

To investigate the capability of delta-radiomics to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This retrospective study enrolled 165 consecutive patients with LARC (training set, n = 116; test set, n = 49) who received nCRT before surgery. All patients underwent pre- and post-nCRT MRI examination from which radiomics features were extracted. A delta-radiomics feature was defined as the percentage change in a radiomics feature from pre- to post-nCRT MRI. A data reduction and feature selection process including the least absolute shrinkage and selection operator algorithm was performed for building T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) delta-radiomics signature. Logistic regression was used to build a T2WI and DWI combined radiomics model. Receiver operating characteristic analysis was performed to assess diagnostic performance. Delong method was used to compare the performance of delta-radiomics model with that of magnetic resonance tumor regression grade (mrTRG).

RESULTS

Twenty-seven of 165 patients (16.4%) achieved pCR. T2WI and DWI delta-radiomics signature, and the combined model showed good predictive performance for pCR. The combined model achieved the highest areas under the receiver operating characteristic curves of 0.91 (95% confidence interval: 0.85-0.98) and 0.91 (95% confidence interval: 0.83-0.99) in the training and test sets, respectively (significantly greater than those for mrTRG; training set, p < 0.001; test set, p = 0.04).

CONCLUSION

MRI-based delta-radiomics can help predict pCR after nCRT in patients with LARC with better performance than mrTRG.

Direct comparison of F-18 FDG PET/CT and MRI to predict pathologic response to neoadjuvant treatment in locally advanced rectal cancer: a meta-analysis

OBJECTIVE

The purpose of the current study was to compare the diagnostic accuracies of F-18 FDG PET/CT and MRI for prediction of pathologic responses to neoadjuvant treatment (NAT) in locally advanced rectal cancer (LARC) patients based on a systematic review and meta-analyses.

METHODS

The PubMed, Cochrane, and Embase databases were searched to identify studies that conducted direct comparisons of the diagnostic performance of F-18 FDG PET/CT and MRI for the prediction of pathologic response to NAT in patients with LARC from the earliest available date of indexing up to July 31, 2020. We determined the sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LR + and LR -), and we constructed summary receiver operating characteristic curves.

RESULTS

In nine studies (427 patients), the pooled sensitivity of F-18 FDG PET/CT was 0.79 (95% CI 0.71-0.86) and the pooled specificity was 0.74 (95% CI 0.60-0.84). LR syntheses yielded an overall LR + of 3.1 (95% CI 1.9-5.0) and an LR - of 0.28 (95% CI 0.18-0.43). The pooled diagnostic odds ratio (DOR) was 11 (95% CI 5-26). The pooled sensitivity of MRI was 0.89 (95% CI 0.77-0.95) and the pooled specificity was 0.66 (95% CI 0.55-0.76). LR syntheses yielded an overall LR + of 2.6 (95% CI 1.9-3.6) and an LR - of 0.17 (95% CI 0.08-0.37). The pooled DOR was 15 (95% CI 6-42). In meta-regression analysis, no variable was identified as the source of the study heterogeneity.

CONCLUSION

F-18 FDG PET/CT and MRI showed similar diagnostic performances for the prediction of pathologic responses to NAT in patients with LARC. However, each modality can be a complement to other rather than being used singly.

Predicting Response to Total Neoadjuvant Treatment (TNT) in Locally Advanced Rectal Cancer Based on Multiparametric Magnetic Resonance Imaging: A Retrospective Study

Purpose

To investigate the potential value of magnetic resonance imaging (MRI) in predicting response relevance to total neoadjuvant treatment (TNT) in locally advanced rectal cancer.

Methods

We analyzed MRI of 71 patients underwent TNT from 2015 to 2017 retrospectively. We categorized the response of TNT as CR (complete response) vs non-CR, and high vs moderate vs low sensitivity. Logistic regression analysis was used to identify the best predictors of response. Diagnostic performance was assessed using receiver operating characteristic curve analysis.

Results

Post-ICT (induction chemotherapy) ∆TL (tumor length), post-CRT (concurrent chemoradiotherapy) ∆LNN (the numbers of lymph node metastases), post-CCT (consolidation chemotherapy) ∆S_DWI (maximum cross-sectional area of tumor on diffusion-weighted imaging), post-CCT ADC_T (the mean apparent diffusion coefficient values of tumor) and post-CCT ∆LNV (volume of lymph node) were the best CR predictors. Post-ICT ∆TL, post-CRT EMVI (extramural vascular invasion) and post-CCT ∆S_T2 (S on T2-weight) were the best significant factors for high sensitivity.

Conclusion

Post-ICT ∆TL may be an early predictor of CR and high sensitivity to TNT. Dynamic analysis based on MRI between baseline and post-CCT could provide the most valuable prediction of CR. The grouping modality of CR vs non-CR may be more suitable for treatment response prediction than high vs moderate vs low sensitivity.

Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma

OBJECTIVES

To investigate the value of volumetric ADC histogram metrics for evaluating lymphovascular space invasion (LVSI) status in stage I endometrioid adenocarcinoma (EAC).

METHODS

Preoperative MRI of 227 patients with stage I EAC were retrospectively analyzed. ADC histogram data were derived from the whole tumor with ROIs drawn on all slices of DWI scans (b = 0, 1000 s/mm²). The Student t-test was performed to compare ADC histogram metrics (minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC; skewness; and kurtosis) between the LVSI-positive and LVSI-negative groups, as well as between stage Ia and Ib EACs. ROC curve analysis was carried out to evaluate the diagnostic performance of ADC histogram metrics in predicting LVSI status in EAC.

RESULTS

The minADC and meanADC and 10th, 25th, 50th, 75th, and 90th percentiles of ADC were significantly lower in LVSI-positive EACs compared with those in the LVSI-negative groups for stage I, Ia, and Ib EACs (all p < 0.05). MeanADC ≤ 0.857 × 10^-3 mm²/s, meanADC ≤ 0.854 × 10^-3 mm²/s, and the 90th percentile of ADC ≤ 1.06 × 10^-3 mm²/s yielded the largest AUC of 0.844, 0.844, and 0.849 for evaluating LVSI positivity in stage I, Ia, and Ib tumors, respectively, with sensitivity of 75.4%, 75.0%, and 76.2%; specificity of 80.0%, 83.1%, and 82.1%; and accuracy of 79.3%, 81.5%, and 79.6%, respectively.

CONCLUSION

Volumetric ADC histogram metrics might be helpful for the preoperative evaluation of LVSI status and personalized clinical management in patients with stage I EAC.

KEY POINTS

• Volumetric ADC histogram analysis helps evaluate LVSI status preoperatively. • LVSI-positive EAC is associated with a reduction in multiple volumetric ADC histogram metrics. • MeanADC and the 90th percentile of ADC were shown to be best in evaluating LVSI- positivity in stage Ia and Ib EACs, respectively.

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.

The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer

PURPOSE

To assess the value of volumetric ADC histogram metrics in evaluating the histological subtype and grade of endometrial cancer.

METHOD

Preoperative MRI datasets of 317 patients with endometrial cancer were used to obtain volumetric ADC histogram metrics (tumour volume; minADC, maxADC and meanADC; 10th, 25th, 50th, 75th and 90th percentiles of ADC; skewness; and kurtosis). The Mann-Whitney test or Student's t-test was used to compare the difference in ADC histogram metrics between endometrioid adenocarcinomas (EACs) and serous endometrial cancers (SECs) and between different tumour grades (G1, G2, G3). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of ADC histogram metrics or combined models in predicting the tumour subtype and grade.

RESULTS

SECs showed a significantly larger tumour volume (P < 0.001) and lower meanADC, 50th, 75th and 90th percentiles of ADC than EACs (all P < 0.05). MinADC, maxADC, meanADC, 10th, 25th, 50th, 75th, 90th percentiles of ADC were significantly higher in G1 than in G2 and G3 EACs (all P < 0.05), while were not significantly different between G2 and G3 EACs (all P > 0.05). A tumour volume ≥ 7.752 cm³ allowed for the prediction of SECs, with an AUC of 0.765 (0.714-0.810). A meanADC ≥ 0.892 × 10^-3  mm²/s enabled to discriminate G1 from G2 and G3 EACs, with an AUC of 0.818 (0.769-0.861).

CONCLUSION

Volumetric ADC histogram analysis is helpful for non-invasive preoperatively predicting the subtype of endometrial cancer and differentiating G1 from G2 and G3 EACs.

Reduced field-of-view versus full field-of-view diffusion-weighted imaging for the evaluation of complete response to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

PURPOSE

To determine whether reduced field-of-view (rFOV) DWI sequences can improve image quality and diagnostic performance compared with conventional full FOV (fFOV) DWI in the prediction of complete response (CR) to neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancers.

METHODS

Between September 2015 and December 2017, seventy-three patients with locally advanced rectal cancers (≥ T3 or lymph node positive) who underwent CRT and subsequent surgery were included in this retrospective study. All patients had tumor located no more than 10 cm from the anal verge, and underwent rectal MRI including fFOV b-1000 DWI and rFOV b-1000 DWI at 3 T before and after CRT. Image quality and diagnostic performance in predicting CR were compared between rFOV DWI and fFOV DWI sets by two reviewers.

RESULTS

Based on a 12-point scale, rFOV DWI provided better image quality scores than fFOV DWI (9.1 ± 1.7 vs. 8.4 ± 1.0, respectively, P < 0.001). Diagnostic accuracy (A_z) in evaluating CR was better with the rFOV DWI set than with the fFOV DWI set for both reviewers: reviewer 1, 0.78 vs. 0.57 (P = .004); reviewer 2, 0.72 vs. 0.61 (P = .031).

CONCLUSION

rFOV DWI of rectal cancer can provide better overall image quality, and its addition to conventional rectal MRI may provide better diagnostic accuracy than fFOV DWI in the evaluation of CR to neoadjuvant CRT in patients with locally advanced rectal cancer.

Comparison of percentage changes in quantitative diffusion parameters for assessing pathological complete response to neoadjuvant therapy in locally advanced rectal cancer: a meta-analysis

PURPOSE

To evaluate and compare the diagnostic performance of percentage changes in apparent diffusion coefficient (∆ADC%) and slow diffusion coefficient (∆D%) for assessing pathological complete response (pCR) to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC).

METHODS

A systematic search in PubMed, EMBASE, the Web of Science, and the Cochrane Library was performed to retrieve related original studies. For each parameter (∆ADC% and ∆D%), we pooled the sensitivity, specificity and calculated the area under summary receiver operating characteristic curve (AUROC) values. Meta-regression and subgroup analyses were performed to explore heterogeneity among the studies on ∆ADC%.

RESULTS

15 original studies (804 patients with 805 lesions, 15 studies on ∆ADC%, 4 of the studies both on ∆ADC% and ∆D%) were included. pCR was observed in 213 lesions (26.46%). For the assessment of pCR, the pooled sensitivity, specificity and AUROC of ∆ADC% were 0.83 (95% confidence intervals [CI] 0.76, 0.89), 0.74 (95% CI 0.66, 0.81), 0.87 (95% CI 0.83, 0.89), and ∆D% were 0.70 (95% CI 0.52, 0.84), 0.81 (95% CI 0.65, 0.90), 0.81 (95% CI 0.77, 0.84), respectively. In the four studies on the both metrics, ∆ADC% yielded an equivalent diagnostic performance (AUROC 0.80 [95% CI 0.76, 0.83]) to ∆D%, but lower than in the studies (n = 11) only on ∆ADC% (AUROC 0.88 [95% CI 0.85, 0.91]). Meta-regression and subgroup analyses showed no significant factors affecting heterogeneity.

CONCLUSIONS

Our meta-analysis confirms that ∆ADC% could reliably evaluate pCR in patients with LARC after neoadjuvant therapy. ∆D% may not be superior to ∆ADC%, which deserves further investigation.

A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

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.

Dual-energy CT parameters in correlation to MRI-based apparent diffusion coefficient: evaluation in rectal cancer after radiochemotherapy

Background

Rectal cancer (RC) is a frequent malignancy for which magnetic resonance imaging (MRI) is the most common and accurate imaging. Iodine concentration (IC) can be quantified with spectral dual-layer computed tomography CT (DL-CT), which could improve imaging of RC, especially for evaluation of response to radiochemotherapy (RCT).

Purpose

To compare a DL-CT system to MRI as the non-invasive imaging gold standard for imaging of RC to evaluate the possibility of a response evaluation with DL-CT.

Material and Methods

Eleven patients who received DL-CT as well as MRI before and after RCT of RC were retrospectively included into this study. For each examination, a region of interest (ROI) was placed within the tumor. For MRI, the mean apparent diffusion coefficient (ADC) was assessed. For DL-CT, IC, z-effective, and Hounsfield Units (HU) were measured. IC, z-effective, and HU were normalized to the aorta. ADC was correlated to absolute and relative normalized IC, z-effective, and HU with Spearman’s ρ. Differences before and after treatment were tested with Wilcoxon signed-rank test.

Results

HU, IC, and Z-effective values in DL-CT images decreased significantly after RCT (P<0.01 for each comparison). The mean ADC increased significantly after RCT. Spearman’s ρ of the absolute IC difference and the absolute ADC (both before and after RCT) is high and significant (ρ = 0.73; P = 0.01), whereas the ρ-value for z-effective (ρ = 0.56) or HU (ρ = 0.45) to ADC was lower and non-significant.

Conclusion

Response evaluation of RC after RCT could be possible with DL-CT via the measurement of IC.

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.

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

Traditional magnetic resonance (MR) diffusion-weighted imaging (DWI) uses a single exponential model to obtain the apparent diffusion coefficient to quantitatively reflect the diffusion motion of water molecules in living tissues, but it is affected by blood perfusion. Intravoxel incoherent motion (IVIM)-DWI utilizes a double-exponential model to obtain information on pure water molecule diffusion and microcirculatory perfusion-related diffusion, which compensates for the insufficiency of traditional DWI. In recent years, research on the application of IVIM-DWI in the diagnosis and treatment of hepatic diseases has gradually increased and has achieved considerable progress. This study mainly reviews the basic principles of IVIM-DWI and related research progress in the diagnosis and treatment of hepatic diseases.

A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy

PURPOSE

Bevacizumab is considered a promising therapy for brain necrosis after radiotherapy, while some patients fail to derive benefit or even worsen. Hence, we developed and validated a radiomics model for predicting the response to bevacizumab in patients with brain necrosis after radiotherapy.

EXPERIMENTAL DESIGN

A total of 149 patients (with 194 brain lesions; 101, 51, and 42 in the training, internal, and external validation sets, respectively) receiving bevacizumab were enrolled. In total, 1,301 radiomic features were extracted from the pretreatment MRI images of each lesion. In the training set, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm. Multivariable logistic regression analysis was then used to develop a radiomics model incorporated in the radiomics signature and independent clinical predictors. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with internal and external validation.

RESULTS

The radiomics signature consisted of 18 selected features and showed good discrimination performance. The model, which integrates the radiomics signature, the interval between radiotherapy and diagnosis of brain necrosis, and the interval between diagnosis of brain necrosis and treatment with bevacizumab, showed favorable calibration and discrimination in the training set (AUC 0.916). These findings were confirmed in the validation sets (AUC 0.912 and 0.827, respectively). Decision curve analysis confirmed the clinical utility of the model.

CONCLUSIONS

The presented radiomics model, available as an online calculator, can serve as a user-friendly tool for individualized prediction of the response to bevacizumab in patients with brain necrosis after radiotherapy.

MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry

AIM

To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (V_T2W) and on DWI at b=1,000 images (V_b,1,000) at each time point, hence the tumour volume reduction rate (ΔV_T2W and ΔV_b,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves.

RESULTS

After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔV_T2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔV_T2W (-82.8%) and ΔV_{b, 1,000} (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively.

CONCLUSIONS

The median ADC value at pre-treatment MRI and ΔV_T2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔV_T2W and ΔV_b,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.

Assessment of histologic prognostic factors of resectable rectal cancer: comparison of diagnostic performance using various apparent diffusion coefficient parameters

This study is to investigate optimum apparent diffusion coefficient (ADC) parameter for predicting lymphovascular invasion (LVI), lymph node metastasis (LNM) and histology type in resectable rectal cancer. 58 consecutive patients with resectable rectal cancer were retrospectively identified. The minimum, maximum, average ADC and ADC difference value were obtained on ADC maps. Maximum ADC and ADC difference value increased with the appearance of LVI (r = 0.501 and 0.495, P < 0.001, respectively) and development of N category (r = 0.615 and 0.695, P < 0.001, respectively). ADC difference value tended to rise with lower tumor differentiation (r = - 0.269, P = 0.041). ADC difference value was an independent risk factor for predicting LVI (odds ratio = 1.323; P = 0.005) and LNM (odds ratio = 1.526; P = 0.005). Maximum ADC and ADC difference value could distinguish N0 from N1 category, N0 from N1-N2, N0-N1 from N2 (all P < 0.001). Only ADC difference value could distinguish histology type (P = 0.041). ADC difference value had higher area under the receiver operating characteristic curve than maximum ADC in identifying LVI (0.828 vs 0.797), N0 from N1 category (0.947 vs 0.847), N0 from N1-N2 (0.935 vs 0.874), and N0-N1 from N2 (0.814 vs 0.770). ADC difference value may be superior to the other ADC value parameters to predict LVI, N category and histology type of resectable rectal cancer.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer

Background

The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature.

Purpose

To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer.

Material and Methods

Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group.

Results

Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix.

Conclusions

Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

MRI Evaluation of the Response of Rectal Cancer to Neoadjuvant Chemoradiation Therapy

MRI plays a critical role in the staging and restaging of rectal cancer. Although newly diagnosed early-stage rectal cancers may immediately be amenable to surgical resection, patients with advanced disease first undergo neoadjuvant therapy that consists of a combination of chemotherapy and radiation therapy. Evaluation of rectal cancer after neoadjuvant therapy is best performed with MRI, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In this setting, MRI allows accurate evaluation of primary tumor staging, which is determined on the basis of the depth of invasion within and through the rectal wall and the involvement of adjacent organs. MRI can also be used to evaluate posttreatment morphologic components within the tumors, including fibrosis and mucinous changes that have been shown to correlate with the response to treatment. Additional features such as the circumferential resection margin and extramural vascular invasion-factors shown to affect prognosis and local recurrence-are also assessed before and after therapy. Functional assessment with diffusion-weighted MRI and perfusion MRI plays a role in predicting tumor aggressiveness and the likelihood of response to treatment, as well as the extent of residual tumor after therapy. Lymph node staging is also performed at MRI, with assessment of not only lymph node size but also the internal architecture and signal intensity characteristics. ^©RSNA, 2019 See discussion on this article by Wasnik and Al-Hawary .

Diagnostic accuracy of b800 and b1500 DWI-MRI of the pelvis to detect residual rectal adenocarcinoma: a multi-reader study

PURPOSE

To compare the sensitivity, specificity and intra-observer and inter-observer agreement of pelvic magnetic resonance imaging (MRI) b800 and b1500 s/mm² sequences in the detection of residual adenocarcinoma after neoadjuvant chemoradiation (CRT) for locally advanced rectal cancer (LARC).

INTRODUCTION

Detection of residual adenocarcinoma after neoadjuvant CRT for LARC has become increasingly important and relies on both MRI and endoscopic surveillance. Optimal MRI diffusion b values have yet to be established for this clinical purpose.

METHODS

From our MRI database between 2018 and 2019, we identified a cohort of 28 patients after exclusions who underwent MRI of the rectum before and after neoadjuvant chemoradiation with a protocol that included both b800 and b1500 s/mm² diffusion sequences. Four radiologists experienced in rectal MRI interpreted the post-CRT MRI studies with either b800 DWI or b1500 DWI, and a minimum of 2 weeks later re-interpreted the same studies using the other b value sequence. Surgical pathology or endoscopic follow-up for 1 year without tumor re-growth was used as the reference standard. Descriptive statistics compared accuracy for each reader and for all readers combined between b values. Inter-observer agreement was assessed using kappa statistics. A p value of 0.05 or less was considered significant.

RESULTS

Within the cohort, 19/28 (67.9%) had residual tumor, while 9/28 (32.1%) had a complete response. Among four readers, one reader had increased sensitivity for detection of residual tumor at b1500 s/mm² (0.737 vs. 0.526, p = 0.046). There was no significant difference between detection of residual tumor at b800 and at b1500 for the rest of the readers. With all readers combined, the pooled sensitivity was 0.724 at b1500 versus 0.605 at b800, but this was not significant (p = 0.119). There was no difference in agreement between readers at the two b value settings (67.8% at b800 vs. 72.0% at b1500), or for any combination of individual readers.

CONCLUSION

Aside from one reader demonstrating increased sensitivity, no significant difference in accuracy parameters or inter-observer agreement was found between MR using b800 and b1500 for the detection of residual tumor after neoadjuvant CRT for LARC. However, there was a suggestion of a trend towards increased sensitivity with b1500, and further studies using larger cohorts may be needed to further investigate this topic.

Diagnostic accuracy of b800 and b1500 DWI-MRI of the pelvis to detect residual rectal adenocarcinoma: a multi-reader study

PURPOSE

To compare the sensitivity, specificity and intra-observer and inter-observer agreement of pelvic magnetic resonance imaging (MRI) b800 and b1500 s/mm² sequences in the detection of residual adenocarcinoma after neoadjuvant chemoradiation (CRT) for locally advanced rectal cancer (LARC).

INTRODUCTION

Detection of residual adenocarcinoma after neoadjuvant CRT for LARC has become increasingly important and relies on both MRI and endoscopic surveillance. Optimal MRI diffusion b values have yet to be established for this clinical purpose.

METHODS

From our MRI database between 2018 and 2019, we identified a cohort of 28 patients after exclusions who underwent MRI of the rectum before and after neoadjuvant chemoradiation with a protocol that included both b800 and b1500 s/mm² diffusion sequences. Four radiologists experienced in rectal MRI interpreted the post-CRT MRI studies with either b800 DWI or b1500 DWI, and a minimum of 2 weeks later re-interpreted the same studies using the other b value sequence. Surgical pathology or endoscopic follow-up for 1 year without tumor re-growth was used as the reference standard. Descriptive statistics compared accuracy for each reader and for all readers combined between b values. Inter-observer agreement was assessed using kappa statistics. A p value of 0.05 or less was considered significant.

RESULTS

Within the cohort, 19/28 (67.9%) had residual tumor, while 9/28 (32.1%) had a complete response. Among four readers, one reader had increased sensitivity for detection of residual tumor at b1500 s/mm² (0.737 vs. 0.526, p = 0.046). There was no significant difference between detection of residual tumor at b800 and at b1500 for the rest of the readers. With all readers combined, the pooled sensitivity was 0.724 at b1500 versus 0.605 at b800, but this was not significant (p = 0.119). There was no difference in agreement between readers at the two b value settings (67.8% at b800 vs. 72.0% at b1500), or for any combination of individual readers.

CONCLUSION

Aside from one reader demonstrating increased sensitivity, no significant difference in accuracy parameters or inter-observer agreement was found between MR using b800 and b1500 for the detection of residual tumor after neoadjuvant CRT for LARC. However, there was a suggestion of a trend towards increased sensitivity with b1500, and further studies using larger cohorts may be needed to further investigate this topic.

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

Background

The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination.

Methods

A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA).

Results

The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model.

Conclusion

The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.

Evaluation of Early Response to Treatment of Hepatocellular Carcinoma with Yttrium-90 Radioembolization Using Quantitative Computed Tomography Analysis

Objective

To identify an imaging predictor for the assessment of early treatment response to yttrium-90 transarterial radioembolization (TARE) in patients with hepatocellular carcinoma (HCC), using a quantitative assessment of dynamic computed tomography (CT) images.

Materials and Methods

Dynamic contrast-enhanced CT was obtained pre- and 4 weeks post-TARE in 44 patients (34 men, 10 women; mean age, 60 years) with HCC. Computer software was developed for measuring the percentage increase in the combined delayed-enhancing area and necrotic area (pD + N) and the percentage increase in the necrotic area (pNI) in the tumor-containing segments pre- and post-TARE. Local progression-free survival (PFS) was compared between patient groups using Cox regression and Kaplan-Meier analyses.

Results

Post-TARE HCC with pD + N ≥ 35.5% showed significantly longer PFS than those with pD + N < 35.5% (p = 0.001). The local tumor progression hazard ratio was 17.3 (p = 0.009) for pD + N < 35.5% versus pD + N ≥ 35.5% groups. HCCs with a high pNI tended to have longer PFS, although this difference did not reach statistical significance.

Conclusion

HCCs with a larger pD + N are less likely to develop local progression after TARE.

Locally advanced rectal cancer: qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging in restaging after neoadjuvant chemo-radiotherapy

PURPOSE

To determine the added value of qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging (DWI) in locally advanced rectal cancer (LARC) restaging after neoadjuvant chemo-radiotherapy (CRT).

MATERIALS AND METHODS

A retrospective study was performed of 21 patients with LARC treated with CRT. All patients were evaluated with 1.5 T conventional magnetic resonance imaging (MRI) and DWI (0-1000 s/mm²) before starting therapy and after neoadjuvant CRT. All included patients underwent surgery after CRT: the histopathological evaluation of surgical specimens represented the reference standard for local staging after neoadjuvant therapy. The qualitative analysis was carried out by two operators in consensus, who reviewed the conventional MR image set [T1-weighted and T2-weighted morphological sequences + dynamic contrast-enhanced sequences (DCE)] and the combined set of conventional and DW images. For the quantitative analysis, the apparent diffusion coefficient (ADC) values were measured at each examination. For each lesion, the mean ADC value (ADCpre and ADCpost) and the ΔADC (ADCpost - ADCpre) were calculated, and values of the three groups of response [complete response (pCR), partial response (pPR), stable disease (pSD)] were compared.

RESULTS

In LARC restaging, conventional MRI showed a sensitivity of 80% and a specificity of 50%, with a total diagnostic capacity of 71.40%, while by adding DWI sensitivity increased to 100%, specificity to 67%, and total diagnostic capacity to 90.40%. ΔADC correlates with treatment response and a cutoff of 1.35 × 10^-3 mm²/s predicts the pCR with a sensitivity of 93.3% and a specificity of 83.3%.

CONCLUSIONS

Adding DWI to conventional sequences may improve MRI capability to evaluate tumor response to CRT. The quantitative DWI assessment is promising, but larger studies are required.

MRI for Restaging Locally Advanced Rectal Cancer: Detailed Analysis of Discrepancies With the Pathologic Reference Standard

OBJECTIVE. The purpose of this study was to analyze causes of discrepancies between restaging MRI and pathologic findings in the assessment of morphologic indicators of tumor response in patients with rectal cancer who have undergone neoadjuvant treatment. MATERIALS AND METHODS. MRI and pathologic data from 57 consecutively registered patients who underwent neoadjuvant treatment and total mesorectal excision between August 2015 and July 2018 were retrospectively analyzed. The sensitivity and specificity of restaging MRI in determining tumor regression grade, T category, N category, circumferential resection margin, and extramural vascular invasion were calculated with pathologic results as the reference standard. One-by-one comparisons between MRI and pathologic findings were conducted to identify causes of discrepancies. RESULTS. The sensitivity of MRI in determining tumor regression grades 3-5 was 77.1%; T3 and T4 category, 100.0%; node-positive disease, 75.0%; circumferential resection margin, 87.5%; and extramural vascular invasion, 91.7%. The specificity values were 72.7%, 62.5%, 70.7%, 85.7%, and 64.4%. Overstaging was mainly caused by misinterpretation of fibrotic areas as residual tumor. Inflammatory cell infiltration could appear as high signal intensity in fibrotic areas on DW images, an appearance similar to that of residual tumor. Edematous mucosa and submucosa adjacent to the tumor and muscularis propria could also be mistaken for residual tumor because of their intermediate signal intensity on T2-weighted MR images. CONCLUSION. MRI was prone to overstaging of disease. Discrepancies between MRI and pathologic findings were mainly caused by misinterpretation of fibrosis. Inflammatory cell infiltration, pure mucin, edematous mucosa and submucosa adjacent to the tumor, and muscularis propria could also be misinterpreted as residual tumor.

[Diffusion-weighted imaging-diagnostic supplement or alternative to contrast agents in early detection of malignancies?]

Medical research in the field of oncologic imaging diagnostics using magnetic resonance imaging increasingly includes diffusion-weighted imaging (DWI) sequences. The DWI sequences allow insights into different microstructural diffusion properties of water molecules in tissues depending on the sequence modification used and enable visual and quantitative analysis of the acquired imaging data. In DWI, the application of intravenous gadolinium-containing contrast agents is unnecessary and only the mobility of naturally occurring water molecules in tissues is quantified. These characteristics predispose DWI as a potential candidate for emerging as an independent diagnostic tool in selected cases and specific points in question. Current clinical diagnostic studies and the ongoing technical developments, including the increasing influence of artificial intelligence in radiology, support the growing importance of DWI. Especially with respect to selective approaches for early detection of malignancies, DWI could make an essential contribution as an eligible diagnostic tool; however, prior to discussing a broader clinical implementation, challenges regarding reliable data quality, standardization and quality assurance must be overcome.

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.