Explore the vast range of titles available.

ObjectivesTo update the 2012 ESGAR consensus guidelines on the acquisition, interpretation and reporting of magnetic resonance imaging (MRI) for clinical staging and restaging of rectal cancer.MethodsFourteen abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) participated in a consensus meeting, organised according to an adaptation of the RAND-UCLA Appropriateness Method. Two independent (non-voting) Chairs facilitated the meeting. 246 items were scored (comprising 229 items from the previous 2012 consensus and 17 additional items) and classified as ‘appropriate’ or ‘inappropriate’ (defined by ≥ 80 % consensus) or uncertain (defined by < 80 % consensus).ResultsConsensus was reached for 226 (92 %) of items. From these recommendations regarding hardware, patient preparation, imaging sequences and acquisition, criteria for MR imaging evaluation and reporting structure were constructed. The main additions to the 2012 consensus include recommendations regarding use of diffusion-weighted imaging, criteria for nodal staging and a recommended structured report template.ConclusionsThese updated expert consensus recommendations should be used as clinical guidelines for primary staging and restaging of rectal cancer using MRI.Key Points• These guidelines present recommendations for staging and reporting of rectal cancer.• The guidelines were constructed through consensus amongst 14 pelvic imaging experts.• Consensus was reached by the experts for 92 % of the 246 items discussed.• Practical guidelines for nodal staging are proposed.• A structured reporting template is presented.

Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable information. However, manual delineation of tumours is a time consuming procedure, as it requires a high level of expertise. Here, we evaluate deep learning methods for automatic localization and segmentation of rectal cancers on multiparametric MR imaging. MRI scans (1.5T, T2-weighted, and DWI) of 140 patients with locally advanced rectal cancer were included in our analysis, equally divided between discovery and validation datasets. Two expert radiologists segmented each tumor. A convolutional neural network (CNN) was trained on the multiparametric MRIs of the discovery set to classify each voxel into tumour or non-tumour. On the independent validation dataset, the CNN showed high segmentation accuracy for reader1 (Dice Similarity Coefficient (DSC = 0.68) and reader2 (DSC = 0.70). The area under the curve (AUC) of the resulting probability maps was very high for both readers, AUC = 0.99 (SD = 0.05). Our results demonstrate that deep learning can perform accurate localization and segmentation of rectal cancer in MR imaging in the majority of patients. Deep learning technologies have the potential to improve the speed and accuracy of MRI-based rectum segmentations.

Purpose In 10–24% of patients with rectal cancer who are treated with neoadjuvant chemoradiation, no residual tumor is found after surgery (ypT0). When accurately selected, these complete responders might be considered for less invasive treatments instead of standard surgery. So far, no imaging method has proven reliable. This study was designed to assess the accuracy of diffusion-weighted MRI (DWI) in addition to standard rectal MRI for selection of complete responders after chemoradiation. Methods A total of 120 patients with locally advanced rectal cancer from three university hospitals underwent chemoradiation followed by a restaging MRI (1.5T), consisting of standard T2W-MRI and DWI (b0-1000). Three independent readers first scored the standard MRI only for the likelihood of a complete response using a 5-point confidence score, after which the DWI images were added and the scoring was repeated. Histology (ypT0 vs. ypT1-4) was the standard reference. Diagnostic performance for selection of complete responders and interobserver agreement were compared for the two readings. Results Twenty-five of 120 patients had a complete response (ypT0). Areas under the ROC-curve for the three readers improved from 0.76, 0.68, and 0.58, using only standard MRI, to 0.8, 0.8, and 0.78 after addition of DWI ( P  = 0.39, 0.02, and 0.002). Sensitivity for selection of complete responders ranged from 0–40% on standard MRI versus 52–64% after addition of DWI. Specificity was equally high (89–98%) for both reading sessions. Interobserver agreement improved from κ 0.2–0.32 on standard MRI to 0.51–0.55 after addition of DWI. Conclusions Addition of DWI to standard rectal MRI improves the selection of complete responders after chemoradiation.

Accurate rectal tumor segmentation using magnetic resonance imaging (MRI) is paramount for effective treatment planning. It allows for volumetric and other quantitative tumor assessments, potentially aiding in prognostication and treatment response evaluation. Manual delineation of rectal tumors and surrounding structures is time-consuming and labor-intensive. Over the past few years, deep learning has shown strong results in automated tumor segmentation in MRI. Current studies on automated rectal tumor segmentation, however, focus solely on tumoral regions without considering the rectal anatomical entities and often lack a solid multicenter external validation. In this study, we improved rectal tumor segmentation by incorporating anomaly maps derived from anatomical inpainting. This inpainting was trained using a U-Net-based model and trained to reconstruct a healthy rectum and mesorectum from prostate T2-weighted images (T2WI). The rectal anomaly maps were generated from the difference between the original rectal and reconstructed pseudo-healthy slices. The derived anomaly maps were used in the downstream tumor segmentation tasks by fusing them as an additional input channel (AAnnUNet). Alternative methods for integrating rectal anatomical knowledge were evaluated as baselines, including Multi-Target nnUNet (MTnnUNet), which added rectum and mesorectum segmentation as auxiliary tasks, and Multi-Channel nnUNet (MCnnUNet), which utilized rectum and mesorectum masks as additional input channels. As part of this study, we benchmarked nine models for rectal tumor segmentation on a large multicenter (num = 705) dataset of preoperative T2WI and nnUNet outperformed the other eight models on the external test. The MTnnUNet demonstrated improvements in both fully-supervised and mixed-supervised settings where human-annoated tumor masks and AI-generated rectum and mesoretum masks were used compared to nnUNet, while the MCnnUNet showed benefits only in the setting where mixed-supervision were used. Importantly, anomaly maps were strongly associated with tumoral regions, and their integration within AAnnUNet led to the best tumor segmentation results across both settings. The effectiveness of AAnnUNet demonstrated the value of the anomaly maps, indicating a promising direction for improving rectal tumor segmentation and model robustness for multicenter data.

Objectives To develop guidelines describing a standardised approach regarding the acquisition, interpretation and reporting of magnetic resonance imaging (MRI) for clinical staging and restaging of rectal cancer. Methods A consensus meeting of 14 abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) was conducted following the RAND-UCLA Appropriateness Method. Two independent (non-voting) chairs facilitated the meeting. Two hundred and thirty-six items were scored by participants for appropriateness and classified subsequently as appropriate or inappropriate (defined by ≥ 80 % consensus) or uncertain (defined by < 80 % consensus). Items not reaching 80 % consensus were noted. Results Consensus was reached for 88 % of items: recommendations regarding hardware, patient preparation, imaging sequences, angulation, criteria for MRI assessment and MRI reporting were constructed from these. Conclusions These expert consensus recommendations can be used as clinical guidelines for primary staging and restaging of rectal cancer using MRI. Key Points • These guidelines recommend standardised imaging for staging and restaging of rectal cancer. • The guidelines were constructed through consensus amongst 14 abdominal imaging experts. • Consensus was reached by in 88 % of 236 items discussed.

In recent years, neoadjuvant chemoradiotherapy (CRT) has become the standard of care for patients with locally advanced rectal cancer. Until recently, patients routinely proceeded to surgical resection after CRT, regardless of the response. Nowadays, treatment is tailored depending on the response to chemoradiotherapy. In patients that respond very well to CRT, organ-preserving treatments such as watch-and-wait are increasingly considered as an alternative to surgery. To facilitate such personalized treatment planning, there is now an increased demand for more detailed radiological response evaluation after chemoradiation. MRI is one of the main tools used to assess response, but has difficulties in assessing response within areas of post-radiation fibrosis. Hence, MR sequences such as diffusion-weighted imaging are increasingly adopted in clinical MR protocols to improve the differentiation between tumor and fibrosis. In this pictorial review, we discuss the strengths and weaknesses of modern MR imaging, including functional imaging sequences such as diffusion-weighted MRI, for response evaluation after chemoradiation treatment and provide the main pearls and pitfalls for image interpretation.

PurposeTo compare the performance of advanced radiomics analysis to morphological assessment by expert radiologists to predict a good or complete response to chemoradiotherapy in rectal cancer using baseline staging MRI.Materials and methodsWe retrospectively assessed the primary staging MRIs [prior to chemoradiotherapy (CRT)] of 133 rectal cancer patients from 2 centers. First, two expert radiologists subjectively estimated the likelihood of achieving a “complete response” (ypT0) and “good response” (TRG 1–2), using a 5-point score (based on TN-stage, MRF/EMVI-status, size/signal/shape). Next, tumor volumes were segmented on high b value DWI (semi-automated, corrected by 2 non-expert and 2-expert readers, resulting in 5 segmentations), copied to the remaining sequences after which a total of 2505 radiomic features were extracted from T2W, low and high b value DWI and ADC. Stability of features for noise due to inter-reader and inter-scanner and protocol variations was assessed using intraclass correlation (ICC) and the Kruskal–Wallis test. Using data from center 1 (n = 86; training set), top 9 features were selected using minimum Redundancy Maximum Relevance and combined in a logistic regression model. Finally, diagnostic performance of the fitted models was assessed on data from center 2 (n = 47; validation set) and compared to the performance of the radiologists.ResultsThe Radiomic models resulted in AUCs of 0.69–0.79 (with similar results for the segmentations performed by expert/non-expert readers) to predict response, results similar to the morphologic prediction by the expert radiologists (AUC 0.67–0.83). Radiomics using semi-automatically generated segmentations (without manual input) did not result in significant predictive performance.ConclusionsRadiomics could predict response to therapy with comparable diagnostic performance as expert radiologists, regardless of whether image segmentation was performed by non-expert or expert readers, indicating that expert input is not required in order for the radiomics workflow to produce significant predictive performance.

To investigate the role of the apparent diffusion coefficient (ADC) as a potential imaging biomarker to predict metastasis (lymph node metastasis and distant metastasis) in colon cancer based on the ADC-value of the primary tumor. Thirty patients (21M, 9F) were included retrospectively. All patients received a 1.5T MRI of the colon including T2 and DWI sequences. ADC maps were calculated for each patient. An expert reader manually delineated all colon tumors to measure mean ADC and histogram metrics (mean, min, max, median, standard deviation (SD), skewness, kurtosis, 5th-95th percentiles) were calculated. Advanced colon cancer was defined as lymph node mestastasis (N+) or distant metastasis (M+). The student Mann Whitney U-test was used to assess the differences between the ADC means of early and advanced colon cancer. To compare the accuracy of lymph node metastasis (N+) prediction based on morpholigical criteria versus ADC-value of the primary tumor, two blinded readers, determined the lymph node metastasis (N0 vs N+) based on morphological criteria. The sensitivity and specificity in predicting lymph node metastasis was calculated for both readers and for the ADC-value of the primary tumor, with histopathology results as the gold standard. There was a significant difference between the mean ADC-value of advanced versus early tumors (p = 0.002). The optimal cut off value was 1179 * 10-3 mm2/s with an area under the curve (AUC) of 0.83 and a sensitivity and specificity of 81% and 86% respectively to predict advanced tumors. Histogram analyses did not add any significant additional value. The sensitivity and specificity for the prediction of lymph node metastasis based on morphological criteria were 40% and 63% for reader 1 and 30% and 88% for reader 2 respectively. The primary tumor ADC-value using 1.179 * 10-3 mm2/s as threshold had a 100% sensitivity and specificity in predicting lymph node metastasis. The ADC-value of the primary tumor has the potential to predict advanced colon cancer, defined as lymph node metastasis or distant metastasis, with lower ADC values significantly associated with advanced tumors. Furthermore the ADC-value of the primary tumor increases the prediction accuracy of lymph node metastasis compared with morphological criteria.

Objectives To assess the value of MRI and diffusion-weighted imaging (DWI) for diagnosing local tumour regrowth during follow-up of organ preservation treatment after chemoradiotherapy for rectal cancer. Methods Seventy-two patients underwent organ preservation treatment (chemoradiotherapy + transanal endoscopic microsurgery or “wait-and-see”) and were followed with MRI including DWI (1.5 T) every 3 -months during the first year and 6 months during following years. Two readers scored each MRI for local regrowth using a confidence level, first on standard MRI, then on standard MRI+DWI. Histology and clinical follow-up were the standard reference. Receiver operating characteristic curves were constructed and areas under the curve (AUC) and corresponding accuracy figures calculated on a per-scan basis. Results Four hundred and forty MRIs were assessed. Twelve patients developed local regrowth. AUC/sensitivity/specificity for standard MRI were 0.95/58 %/98 % (R1) and 0.96/58 % /100 % (R2). For standard MRI+DWI, these numbers were 0.86/75 %/97 % (R1) and 0.98/75 %/100 % (R2). After adding DWI, the number of equivocal scores decreased from 22 to 7 (R1) and from 40 to 20 (R2). Conclusions Although there was no overall improvement in diagnostic performance in terms of AUC, adding DWI improved the sensitivity of MRI for diagnosing local tumour regrowth and lowered the rate of equivocal MRIs. Key Points • DWI improves sensitivity for detecting local tumour regrowth after organ preservation treatment. • In particular, DWI can aid in detecting small local recurrence. • DWI reduces the number of equivocal scores.

A good understanding of the MRI anatomy of the rectum and its surroundings is pivotal to ensure high-quality diagnostic evaluation and reporting of rectal cancer. With this pictorial review, we aim to provide an image-based overview of key anatomical concepts essential for treatment planning, response evaluation and post-operative assessment. These concepts include the cross-sectional anatomy of the rectal wall in relation to T-staging; differences in staging and treatment between anal and rectal cancer; landmarks used to define the upper and lower boundaries of the rectum; the anatomy of the pelvic floor and anal canal, the mesorectal fascia, peritoneum and peritoneal reflection; and guides to help discern different pelvic lymph node stations on MRI to properly stage regional and non-regional rectal lymph node metastases. Finally, this review will highlight key aspects of post-treatment anatomy, including the assessment of radiation-induced changes and the evaluation of the post-operative pelvis after different surgical resection and reconstruction techniques.Key pointsMRI plays a key role in the staging and treatment stratification of rectal cancer.Understanding rectal and pelvic MRI anatomy is pivotal for high-quality reporting.This review addresses key anatomy concepts for staging, treatment planning, and follow-up.