Reproducibility of rectal tumor volume delineation using diffusion-weighted MRI: Agreement on volumes between observers

Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologist in musculoskeletal tumor imaging using magnetic resonance

Background

Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors.

Aim

This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist.

Methods

Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet's AC1 coefficients and interpreted according to Landis and Koch.

Results

The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772-0.965 among only radiologists indicating substantial to almost perfect agreement.

Conclusion

The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI

INTRODUCTION

The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency.

METHODS

A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated.

RESULTS

The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139).

CONCLUSION

Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

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.

Radiological Biomarkers in MRI directed Rectal Cancer Radiotherapy Volume Delineation

Our study evaluated whether an MRI reporting system highlighting areas of contiguous and discontinuous extramural venous invasion (EMVI) can improve the accuracy of gross tumour volume (GTV) delineation. Initially, 27 consecutive patients with locally advanced rectal cancer treated between 2012 and 2014 were evaluated. We used an MRI reporting proforma that documented the position of the primary tumour, lymph nodes and EMVI. The new GTVs delineated were compared with historical radiotherapy treatment volumes to identify the frequency of GTV geographical miss. We observed that the delineation of involved nodes and areas of EMVI was more likely to represent sources of uncertainty wherein nodal GTV geographical miss was evident in 5 out of 27 patients (19%). Complete EMVI GTV geographical miss occurred in two patients (7%). We re-evaluated our radiotherapy practice in a further 27 patients after the implementation of a modified MRI reporting system. An improvement was seen; nodal miss was observed in two patients (7%) and partial EMVI miss in one patient (4%), although these areas were encompassed in the planning target volume (PTV). Our study shows that extramural venous invasion and involved nodes need to be highlighted on MRI to improve the accuracy of rectal cancer GTV delineation.

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.

Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences

BACKGROUND

The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC.

PURPOSE

To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values.

MATERIAL AND METHODS

A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values.

RESULTS

Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10^-6 mm²/s, and the coefficient of high-grade tumors was 1444 ± 69×10^-6 mm²/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10^-6 mm²/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10^-6 mm²/s.

CONCLUSION

The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.

Conspicuity and muscle-invasiveness assessment for bladder cancer using VI-RADS: a multi-reader, contrast-free MRI study to determine optimal b-values for diffusion-weighted imaging

OBJECTIVE

To (1) compare bladder cancer (BC) muscle invasiveness among three b-values using a contrast-free approach based on Vesical Imaging-Reporting and Data System (VI-RADS), to (2) determine if muscle-invasiveness assessment is affected by the reader experience, and to (3) compare BC conspicuity among three b-values, qualitatively and quantitatively.

METHODS

Thirty-eight patients who underwent a bladder MRI on a 3.0-T scanner were enrolled. The gold standard was histopathology report following transurethral resection of BC. Three sets of images, including T2w and different b-values for DWI, set 1 (b = 1000 s/mm2), set 2 (b = 1500 s/mm2), and set 3 (b = 2000 s/mm2), were reviewed by three differently experienced readers. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. Comparisons among readers and DWI sets were performed with the Wilcoxon test. Receiver operating characteristic (ROC) analysis was performed. Areas under the curves (AUCs) and pairwise comparison were calculated.

RESULTS

AUCs of muscle-invasiveness assessment ranged from 0.896 to 0.984 (reader 1), 0.952-0.968 (reader 2), and 0.952-0.984 (reader 3) without significant differences among different sets and readers (p > 0.05). The mean conspicuity qualitative scores were higher in Set 1 (2.21-2.33), followed by Set 2 (2-2.16) and Set 3 (1.82-2.14). The quantitative conspicuity assessment showed that mean normalized intensity of tumor was significantly higher in Set 2 (4.217-4.737) than in Set 1 (3.923-4.492) and Set 3 (3.833-3.992) (p < 0.05).

CONCLUSION

Muscle invasiveness can be assessed with high accuracy using a contrast-free protocol with T2W and DWI, regardless of reader's experience. b = 1500 s/mm2 showed the best tumor delineation, while b = 1000 s/mm2 allowed for better tumor-wall interface assessment.

Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

PURPOSE

This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo‑radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice.

METHODS

A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics.

RESULTS

The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model.

CONCLUSIONS

The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.

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.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

Volume Delineation in Cervical Cancer With T2 and Diffusion-weighted MRI: Agreement on Volumes Between Observers

AIM

To delineate cervical cancer gross tumor volume (GTV) on T2-magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps, assessing volumes and inter-observer agreement between two observers.

PATIENTS AND METHODS

A radiologist and a radiation oncologist delineated GTV on T2 (T2_GTV) and ADC (ADC_GTV) sequences. Dice similarity index (DICE) and Bland-Altman analysis were used to estimated concordance.

RESULTS

Mean T2_GTV and ADC_GTV volumes were 43.84±71.47 cc and 37.28±68.92 cc according to the radiologist, and 43.4±70.44 cc and 36.65±69.21 cc according to the radiation oncologist. ADC led to statistically significantly smaller volumes compared to T2. The mean DICE index was 0.86 for T2_GTV and 0.84 for ADC_GTV The Bland-Altman plots globally showed concordance.

CONCLUSION

GTV delineation was smaller in the ADC maps compared to T2-MRI, reaching an almost perfect agreement between observers. Thanks to this acceptable variability, adding functional imaging might provide more information for tumor delineation, improving reproducibility for image-guided adaptive radiotherapy.

Morphometric assessment of the mesorectal fat in Chinese Han population: A clinical MRI study

The study aimed to analyze morphometric assessment of the mesorectal fat thickness and its correlation with body mass index in Chinese Han population. The anterior, posterior, right lateral, and left lateral mesorectal fat thickness were measured using MRI T2-weighted images. The mean distance from the rectal wall to the mesorectal fascia were 3.8, 8.4, 11.3, and 11.7 mm in anterior, posterior, right lateral, and left lateral portion, respectively. The mesorectal area, rectal area, mesorectal fat thickness area, and rectal height were 2395.3 ± 691.1 mm2, 709.6 ± 403.5 mm2, 1685.7 ± 525.3 mm2, and 9.1 ± 0.8 cm. BMI was found to be directly proportional to and statistically significant to the mesorectal fat area (p = 0.01). Since the mean mesorectal fat thickness was found to be <12 mm, T3d staged rectal cancer is less likely to be found in an average Chinese population that may affect the overall-survival and progression-free survival in rectal cancer patients. Anterior portion of the rectum was least thick compared to all other sides. Therefore, extra-caution should be taken in handling tumors on the anterior part of the rectum.

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10^–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

PURPOSE

To evaluate the role of IVIM and diffusion kurtosis imaging (DKI) in identifying pathologic complete response (pCR) and T stages after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHOD

Forty-two patients with biopsy-proven rectal adenocarcinoma, who underwent both pre-and post-CRT MRI with IVIM and DKI sequences on a 3 T scanner, were enrolled prospectively. According to the pathologic ypTNM stages and tumor regression grade (TRG), patients were grouped into pCR (TRG0) and non-pCR (TRG1-3) groups and low T stage (ypT0-2) and high T stage (ypT3-4) groups. IVIM parameters (the slow diffusion coefficient [D], fast diffusion coefficient [D*], perfusion fraction [f]), DKI parameters (mean diffusivity [MD] and mean kurtosis [MK]), and mono-exponential ADC were calculated and analyzed between groups.

RESULTS

The pCR group had significantly higher post-CRT ADC, D*, f, and MD values than non-pCR group, and higher percent changes in the ADC, f, and MD values (all P < 0.05). The post-CRT MD values yielded the highest AUC (0.788) with higher sensitivity than post-ADC values (82.9 % vs. 77.1 %, respectively). Post-CRT ADC and MD values and the percent changes in the ADC and MD values were also negatively correlated with TRG (all P < 0.05). Besides, negative correlations were found among the pre-CRT MD, post-CRT ADC, D, f, and MD values and the ypT stages (all P < 0.05).

CONCLUSIONS

Both IVIM and DKI parameters could provide more information when evaluating pCR and T stages after nCRT. In particular, the diagnostic performance of the MD values was more valuable than ADC values in being able to determine pCR.

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

PURPOSE

To compare deep learning with radiologists when diagnosing uterine cervical cancer on a single T2-weighted image.

METHODS

This study included 418 patients (age range, 21-91 years; mean, 50.2 years) who underwent magnetic resonance imaging (MRI) between June 2013 and May 2020. We included 177 patients with pathologically confirmed cervical cancer and 241 non-cancer patients. Sagittal T2-weighted images were used for analysis. A deep learning model using convolutional neural networks (DCNN), called Xception architecture, was trained with 50 epochs using 488 images from 117 cancer patients and 509 images from 181 non-cancer patients. It was tested with 60 images for 60 cancer and 60 non-cancer patients. Three blinded experienced radiologists also interpreted these 120 images independently. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the DCNN model and radiologists.

RESULTS

The DCNN model and the radiologists had a sensitivity of 0.883 and 0.783-0.867, a specificity of 0.933 and 0.917-0.950, and an accuracy of 0.908 and 0.867-0.892, respectively. The DCNN model had an equal to, or better, diagnostic performance than the radiologists (AUC = 0.932, and p for accuracy = 0.272-0.62).

CONCLUSION

Deep learning provided diagnostic performance equivalent to experienced radiologists when diagnosing cervical cancer on a single T2-weighted image.

Manual and semi-automated delineation of locally advanced rectal cancer subvolumes with diffusion-weighted MRI

OBJECTIVES

To evaluate interobserver agreement for T₂ weighted (T₂W) and diffusion-weighted MRI (DW-MRI) contours of locally advanced rectal cancer (LARC); and to evaluate manual and semi-automated delineations of restricted diffusion tumour subvolumes.

METHODS

20 cases of LARC were reviewed by 2 radiation oncologists and 2 radiologists. Contours of gross tumour volume (GTV) on T₂W, DW-MRI and co-registered T₂W/DW-MRI were independently delineated and compared using Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics of interobserver agreement. Restricted diffusion subvolumes within GTVs were manually delineated and compared to semi-automatically generated contours corresponding to intratumoral apparent diffusion coefficient (ADC) centile values.

RESULTS

Observers were able to delineate subvolumes of restricted diffusion with moderate agreement (DSC 0.666, MDA 1.92 mm). Semi-automated segmentation based on the 40th centile intratumoral ADC value demonstrated moderate average agreement with consensus delineations (DSC 0.581, MDA 2.44 mm), with errors noted in image registration and luminal variation between acquisitions. A small validation set of four cases with optimised planning MRI demonstrated improvement (DSC 0.669, MDA 1.91 mm).

CONCLUSION

Contours based on co-registered T₂W and DW-MRI could be used for delineation of biologically relevant tumour subvolumes. Semi-automated delineation based on patient-specific intratumoral ADC thresholds may standardise subvolume delineation if registration between acquisitions is sufficiently accurate.

ADVANCES IN KNOWLEDGE

This is the first study to evaluate the feasibility of semi-automated diffusion-based subvolume delineation in LARC. This approach could be applied to dose escalation or 'dose painting' protocols to improve delineation reproducibility.

The apparent diffusion coefficient is a useful biomarker in predicting treatment response in patients with locally advanced rectal cancer

BACKGROUND

Apparent diffusion coefficient (ADC) values achieve promising results in treatment response prediction in patients with several types of cancers.

PURPOSE

To determine whether ADC values predict neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Forty-four patients with LARC who underwent magnetic resonance imaging scans before and after nCRT followed by delayed surgery were enrolled retrospectively. The sample was distributed as follows: responders (R), n = 8; and non-responders (Non-R), n = 36. Three markers of treatment response were considered: post-nCRT measures; ΔADC; and Δ%ADC. Statistical analysis included a Wilcoxon test, a Mann-Whitney U test, and a receiver operating characteristic (ROC) analysis in order to evaluate the diagnostic accuracy for each ADC value marker to differentiate between R and Non-R.

RESULTS

Both minimum and mean ADC values were significantly higher after nCRT in the R group, while non-significant differences between basal and control ADC values were found in the non-R group. In addition, ΔADC and Δ%ADC exhibited increased values after nCRT in R when compared with non-R. ROC analysis revealed the following diagnostic performance parameters: post-nCRT: ADCmin = 1.05 × 10-3 mm2/s (sensitivity 61.1% and specificity 66.7%), ADCmean = 1.50 × 10-3 mm2/s (sensitivity 72.2% and specificity 83.3%), ΔADC: ADCmin = 0.35 (sensitivity 66.7% and specificity 83.3%), ADCmean = 0.50 (sensitivity 72% and specificity 83%); and Δ%ADC: ADCmin = 44% (sensitivity 66.7% and specificity 83.3%) and ADCmean = 60% (sensitivity 83% and specificity 99%).

CONCLUSION

Our findings suggest that post-treatment rectal tumor ADC values, as well changes between pre- and post-treatment values, may be biomarkers for predicting treatment response in patients with LARC who underwent nCRT.

MRI for Rectal Cancer Primary Staging and Restaging After Neoadjuvant Chemoradiation Therapy: How to Do It During Daily Clinical Practice

PURPOSE

To provide a practical overview regarding the state-of-the-art of the magnetic resonance imaging (MRI) protocol for rectal cancer imaging and interpretation during primary staging and restaging after neoadjuvant chemoradiation therapy (CRT), pointing out technical skills and findings that radiologists should consider for their reports during everyday clinical activity.

METHOD

Both 1.5T and 3.0T scanners can be used for rectal cancer evaluation, using pelvic phased array external coils. The standard MR protocol includes T2-weighted imaging of the pelvis, high-resolution T2-weighted sequences focused on the tumor and diffusion-weighted imaging (DWI). The mnemonic DISTANCE is helpful for the interpretation of MR images: DIS, for distance from the inferior part of the tumor to the anorectal-junction; T, for T staging; A, for anal sphincter complex status; N, for nodal staging; C, for circumferential resection margin status; and E, for extramural venous invasion.

RESULTS

Primary staging with MRI is a cornerstone in the preoperative workup of patients with rectal cancer, because it provides clue information for decisions on the administration of CRT and surgical treatment. Restaging after CRT is crucial for treatment planning, and findings on post-CRT MRI correlate with the patient's prognosis and survival. It may be useful to remember the mnemonic word "DISTANCE" to check and describe all the relevant MRI findings necessary for an accurate radiological definition of tumor stage and response to CRT.

CONCLUSIONS

"DISTANCE" assessment for rectal cancer staging and treatment response estimation after CRT may be helpful as a checklist for a structured reporting.

Gross tumour volume delineation in anal cancer on T2-weighted and diffusion-weighted MRI - Reproducibility between radiologists and radiation oncologists and impact of reader experience level and DWI image quality

PURPOSE

To assess how gross tumour volume (GTV) delineation in anal cancer is affected by interobserver variations between radiologists and radiation oncologists, expertise level, and use of T2-weighted MRI (T2W-MRI) vs. diffusion-weighted imaging (DWI), and to explore effects of DWI quality.

METHODS AND MATERIALS

We retrospectively analyzed the MRIs (T2W-MRI and b800-DWI) of 25 anal cancer patients. Four readers (Senior and Junior Radiologist; Senior and Junior Radiation Oncologist) independently delineated GTVs, first on T2W-MRI only and then on DWI (with reference to T2W-MRI). Maximum Tumour Diameter (MTD) was calculated from each GTV. Mean GTVs/MTDs were compared between readers and between T2W-MRI vs. DWI. Interobserver agreement was calculated as Intraclass Correlation Coefficient (ICC), Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). DWI image quality was assessed using a 5-point artefact scale.

RESULTS

Interobserver agreement between radiologists vs. radiation oncologists and between junior vs. senior readers was good-excellent, with similar agreement for T2W-MRI and DWI (e.g. ICCs 0.72-0.94 for T2W-MRI and 0.68-0.89 for DWI). There was a trend towards smaller GTVs on DWI, but only for the radiologists (P = 0.03-0.07). Moderate-severe DWI-artefacts were observed in 11/25 (44%) cases. Agreement tended to be lower in these cases.

CONCLUSION

Overall interobserver agreement for anal cancer GTV delineation on MRI is good for both radiologists and radiation oncologists, regardless of experience level. Use of DWI did not improve agreement. DWI artefacts affecting GTV delineation occurred in almost half of the patients, which may severely limit the use of DWI for radiotherapy planning if no steps are undertaken to avoid them.