Extramural Venous Invasion and Tumor Deposit at Diffusion-weighted MRI in Patients after Neoadjuvant Treatment for Rectal Cancer.

Background Diffusion-weighted (DW) imaging is useful in detecting tumor in the primary tumor bed in locally advanced rectal cancer (LARC) after neoadjuvant therapy, but its value in detecting extramural venous invasion (EMVI) and tumor deposit is not well validated. Purpose To evaluate diagnostic accuracy and association with patient prognosis of viable EMVI and tumor deposit on DW images in patients with LARC after neoadjuvant therapy using whole-mount pathology specimens. Materials and Methods This retrospective study included patients who underwent neoadjuvant therapy and surgery from 2018 to 2021. Innovative five-point Likert scale was used by two radiologists to independently evaluate the likelihood of viable EMVI and tumor deposit on restaging DW MRI scans in four axial quadrants (12 to 3 o'clock, 3 to 6 o'clock, 6 to 9 o'clock, and 9 to 12 o'clock). Diagnostic accuracy was assessed at both the per-quadrant and per-patient level, with whole-mount pathology as the reference standard. Weighted κ values for interreader agreement and Cox regression models for disease-free survival and overall survival analyses were used. Results A total of 117 patients (mean age, 56 years ± 12 [SD]; 70 male, 47 female) were included. Pathologically proven viable EMVI and tumor deposit was detected in 29 of 117 patients (25%) and in 44 of 468 quadrants (9.4%). Per-quadrant analyses showed an area under the receiver operating characteristics curve of 0.75 (95% CI: 0.68, 0.83), with sensitivity and specificity of 55% and 96%, respectively. Good interreader agreement was observed between the radiologists (κ = 0.62). Per-patient analysis showed sensitivity and specificity of 62% and 93%, respectively. The presence of EMVI and tumor deposit on restaging DW MRI scans was associated with worse disease-free survival (hazard ratio [HR], 5.6; 95% CI: 2.4, 13.3) and overall survival (HR, 8.9; 95% CI: 1.6, 48.5). Conclusion DW imaging using the five-point Likert scale showed high specificity and moderate sensitivity in the detection of viable extramural venous invasion and tumor deposits in LARC after neoadjuvant therapy, and its presence on restaging DW MRI scans is associated with worse prognosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Méndez and Ayuso in this issue.

Radiologic T staging of colon cancer: renewed interest for clinical practice.

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer.

Imaging of Anal Squamous Cell Carcinoma: Survey Results and Expert Opinion from the Rectal and Anal Cancer Disease-Focused Panel of the Society of Abdominal Radiology.

The role and method of image-based staging of anal cancer has evolved with the rapid development of newer imaging modalities and the need to address the rising incidence of this rare cancer. In 2014, the European Society of Medical Oncology mandated pelvic magnetic resonance imaging (MRI) for anal cancer and subsequently other societies such as the National Comprehensive Cancer Network followed suit with similar recommendations. Nevertheless, great variability exists from center to center and even within individual centers. Notably, this is in stark contrast to the imaging of the anatomically nearby rectal cancer. As participating team members for this malignancy, we embarked on a comprehensive literature review of anal cancer imaging to understand the relative merits of these new technologies which developed after computed tomography (CT), e.g., MRI and positron emission tomography/computed tomography (PET/CT). The results of this literature review helped to inform our next stage: questionnaire development regarding the imaging of anal cancer. Next, we distributed the questionnaire to members of the Society of Abdominal Radiology (SAR) Rectal and Anal Disease-Focused Panel, a group of abdominal radiologists with special interest, experience, and expertise in rectal and anal cancer, to provide expert radiologist opinion on the appropriate anal cancer imaging strategy. In our expert opinion survey, experts advocated the use of MRI in general (65% overall and 91-100% for primary staging clinical scenarios) and acknowledged the superiority of PET/CT for nodal assessment (52-56% agreement for using PET/CT in primary staging clinical scenarios compared to 30% for using MRI). We therefore support the use of MRI and PET and suggest further exploration of PET/MRI as an optimal combined evaluation. Our questionnaire responses emphasized the heterogeneity in imaging practice as performed at numerous academic cancer centers across the United States and underscore the need for further reconciliation and establishment of best imaging practice guidelines for optimized patient care in anal cancer.

MRI Assessment of Extramural Venous Invasion Before and After Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer and Its Association with Disease-Free and Overall Survival.

BACKGROUND: Extramural venous invasion (EMVI) on baseline MRI is associated with poor prognosis in patients with locally advanced rectal cancer. This study investigated the association of persistent EMVI after total neoadjuvant therapy (TNT) (chemoradiotherapy and systemic chemotherapy) with survival. METHODS: Baseline MRI, post-TNT MRI, and surgical pathology data from 175 patients with locally advanced rectal cancer who underwent TNT and total mesorectal excision between 2010 and 2017 were retrospectively analyzed for evidence of EMVI. Two radiologists assessed EMVI status with disagreement adjudicated by a third. Pathologic EMVI status was assessed per departmental standards. Cox regression models evaluated the associations between EMVI and disease-free and overall survival. RESULTS: EMVI regression on both post-TNT MRI and surgical pathology was associated with disease-free survival (hazard ratio, 0.17; 95% confidence interval (CI), 0.04-0.64) and overall survival (hazard ratio, 0.11; 95% CI, 0.02-0.68). In an exploratory analysis of 35 patients with EMVI on baseline MRI, only six had EMVI on pathology compared with 18 on post-TNT MRI; these findings were not associated (p = 0.2). Longer disease-free survival was seen with regression on both modalities compared with remaining positive. Regression on pathology alone, independent of MRI EMVI status, was associated with similar improvements in survival. CONCLUSIONS: Baseline EMVI is associated with poor prognosis even after TNT. EMVI regression on surgical pathology is common even with persistent EMVI on post-TNT MRI. EMVI regression on surgical pathology is associated with improved DFS, while the utility of post-TNT MRI EMVI persistence for decision-making and prognosis remains unclear.

Multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE) in diffusion-weighted imaging for rectal MRI: a quantitative and qualitative analysis at multiple b-values.

PURPOSE: To compare four diffusion-weighted imaging (DWI) sequences for image quality, rectal contour, and lesion conspicuity, and to assess the difference in their signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC). METHODS: In this retrospective study of 36 consecutive patients who underwent 3.0 T rectal MRI from January-June 2020, DWI was performed with single-shot echo planar imaging (ss-EPI) (b800 s/mm2), multiplexed sensitivity encoding (MUSE) (b800 s/mm2), MUSE (b1500 s/mm2), and field-of-view optimized and constrained undistorted single-shot (FOCUS) (b1500 s/mm2). Two radiologists independently scored image quality using a 5-point Likert scale. Inter-reader agreement was assessed using the weighted Cohen's к. SNR, CNR, and ADC measurements were compared using the paired t-test. RESULTS: For both readers, MUSE b800 scored significantly higher for image quality, rectal contour, and lesion conspicuity compared to ss-EPI; MUSE b800 also scored significantly higher for image quality and rectal contour compared to all other sequences. Lesion conspicuity was equally superior for MUSE b800 and MUSE b1500 compared to the other two sequences. There was good to excellent inter-reader agreement for all qualitative features (к = 0.72-0.88). MUSE b800 had the highest SNR; MUSE b1500 had the highest CNR. A significant difference in ADC was observed between ss-EPI compared to the other sequences (p < 0.001) and between MUSE b800 and FOCUS. No significant difference in ADC was found between MUSE b1500 and FOCUS b1500. CONCLUSION: MUSE b800 improved image quality over ss-EPI and both MUSE b800 and b1500 showed better tumor conspicuity compared to conventional ss-EPI.

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause (n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm2 vs 197.6 cm2, p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without (n = 1303) and with (n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased (p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased (p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death (p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.

Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer.

PURPOSE: Natural language processing (NLP) applied to radiology reports can help identify clinically relevant M1 subcategories of patients with colorectal cancer (CRC). The primary purpose was to compare the overall survival (OS) of CRC according to American Joint Committee on Cancer TNM staging and explore an alternative classification. The secondary objective was to estimate the frequency of metastasis for each organ. METHODS: Retrospective study of CRC who underwent computed tomography (CT) chest, abdomen, and pelvis between July 1, 2009, and March 26, 2019, at a tertiary cancer center, previously labeled for the presence or absence of metastasis by an NLP prediction model. Patients were classified in M0, M1a, M1b, and M1c (American Joint Committee on Cancer), or an alternative classification on the basis of the metastasis organ number: M1, single; M2, two; M3, three or more organs. Cox regression models were used to estimate hazard ratios; Kaplan-Meier curves were used to visualize survival curves using the two M1 subclassifications. RESULTS: Nine thousand nine hundred twenty-eight patients with a total of 48,408 CT chest, abdomen, and pelvis reports were included. On the basis of NLP prediction, the median OS of M1a, M1b, and M1c was 4.47, 1.72, and 1.52 years, respectively. The median OS of M1, M2, and M3 was 4.24, 2.05, and 1.04 years, respectively. Metastases occurred most often in liver (35.8%), abdominopelvic lymph nodes (32.9%), lungs (29.3%), peritoneum (22.0%), thoracic nodes (19.9%), bones (9.2%), and pelvic organs (7.5%). Spleen and adrenal metastases occurred in < 5%. CONCLUSION: NLP applied to a large radiology report database can identify clinically relevant metastatic phenotypes and be used to investigate new M1 substaging for CRC. Patients with three or more metastatic disease organs have the worst prognosis, with an OS of 1 year.

Use of MR in Pancreaticobiliary Emergencies.

This article presents the MR protocols, imaging features, diagnostic criteria, and complications of commonly encountered emergencies in pancreaticobiliary imaging, which include pancreatic trauma, bile leak, acute cholecystitis, biliary obstruction, and pancreatitis. Various classifications and complications that can arise with these conditions, as well as artifacts that may mimic pathology, are also included. Finally, the emerging utility of abbreviated MR protocols is discussed.

CT Imaging Findings in Patients with Ovarian Cancer and Acute Abdominal Symptoms: Experience at a Tertiary Cancer Center.

PURPOSE: To evaluate computed tomography (CT) findings in patients with ovarian cancer presenting to a comprehensive cancer center's urgent care unit with acute abdominal symptoms. METHODS: This retrospective study included consecutive patients with ovarian cancer who underwent abdominal CT at a comprehensive cancer center's urgent care unit between January 1, 2018, and January 14, 2020, due to acute abdominal symptoms. Two abdominal radiologists reviewed the abdominal CT reports, categorizing imaging findings as follows: (a) no new or acute finding, (b) new or increased bowel or gastric obstruction, (c) new or increased ascites, (d) new or increased peritoneal carcinomatosis, (e) new or increased nonperitoneal metastases, (f) new inflammatory or infectious changes, (g) new or increased hydronephrosis, (h) new or increased biliary dilatation, (i) new vascular complications, or (j) new bowel perforation. RESULTS: A total of 200 patients (mean age, 59 years; range, 22-87) underwent a total of 259 abdominal CT scans, of which 217/259 (83.8%) scans were found to have new or increased findings. A total of 115/259 (44.4%) scans had only one finding while 102/259 (39.4%) scans had 2 or more findings. Altogether, 382 new or increased findings were detected: findings were most commonly related to bowel or gastric obstruction (92/382, 24.1%) with small bowel obstruction being the most common finding (80/382, 20.9%); ascites (78/382, 20.4%); peritoneal carcinomatosis (62/382, 16.2%); and nonperitoneal metastases (62/382, 16.2%). Inflammatory or infectious findings accounted for 30/382 (7.9%) findings. CONCLUSION: Most patients with ovarian cancer presenting with acute abdominal had relevant positive findings on abdominal CT, with small bowel obstruction being the most common finding.

Update to the structured MRI report for primary staging of rectal cancer : Perspective from the SAR Disease Focused Panel on Rectal and Anal Cancer.

OBJECTIVE: To review existing structured MRI reports for primary staging of rectal cancer and create a new, freely available structured report based on multidisciplinary expert opinion and literature review. METHODS: Twenty abdominal imaging experts from the Society of Abdominal Radiology (SAR)'s Disease Focused Panel (DFP) on Rectal and Anal Cancer completed a questionnaire and participated in a subsequent consensus meeting based on the RAND-UCLA Appropriateness Method. Twenty-two items were classified via a group survey as "appropriate" or "inappropriate" (defined by ≥ 70% consensus), or "needs group discussion" (defined by < 70% consensus). Certain items were also discussed with multidisciplinary team members from colorectal surgery, oncology and pathology. RESULTS: After completion of the questionnaire, 16 (72%) items required further discussion (< 70% consensus). Following group discussion, consensus was achieved for 21 (95%) of the items. Based on the consensus meeting, a revised structured report was developed. The most significant modifications included (1) Exclusion of the T2/early T3 category; (2) Replacement of the term "circumferential resection margin (CRM)" with "mesorectal fascia (MRF)"; (3) A revised definition of "mucinous content"; (4) Creation of two distinct categories for suspicious lymph nodes (LNs) and tumor deposits; and (5) Classification of suspicious extra-mesorectal LNs by anatomic location. CONCLUSION: The SAR DFP on Rectal and Anal Cancer recommends using this newly updated reporting template for primary MRI staging of rectal cancer.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation.

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT examinations for staging, treatment response, and surveillance, providing the opportunity for quantitative body composition assessment to be performed as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semiautomated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.

Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.

PURPOSE: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. METHODS: Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. RESULTS: Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). CONCLUSION: We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.

Pancreatic neuroendocrine neoplasms: a 2022 update for radiologists.

Pancreatic neuroendocrine neoplasms (PaNENs) are a unique group of pancreatic neoplasms with a wide range of clinical presentations and behaviors. Given their heterogeneous appearance and increasing detection on cross-sectional imaging, it is essential that radiologists understand the variable presentation and distinctions PaNENs display compared to other pancreatic neoplasms. Additionally, some of these neoplasms may be hormonally functional, and it is imperative that radiologists be aware of the common clinical presentations of hormonally active PaNENs. Knowledge of PaNEN pathology and treatments may influence which imaging modality is optimal for each patient. Each imaging modality used for PaNENs has distinct advantages and disadvantages, particularly in different treatment settings. Thus, the focus of this manuscript is to provide an update for the radiologist on PaNEN pathology, imaging, and treatments.

Occurrence of peritoneal carcinomatosis in patients with rectal cancer undergoing staging pelvic MRI: clinical observations.

OBJECTIVES: Describe the cumulative incidence (CUIN) of peritoneal carcinomatosis (PC) and survival in patients presenting with advanced rectal cancer at staging pelvic MRI. METHODS: From 2013 to 2018, clinicopathologic records of patients with pretreatment rectal MRI clinical (c)T3c, cT3d, cT4a, and cT4b primary rectal adenocarcinoma were retrospectively reviewed by two radiologists. Standard MRI descriptors and pathologic stages were recorded. Recurrence-free (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Development of PC was explored using competing risk analysis. Differences in survival were compared using the log-rank test. Gray's test was used to test for differences in CUIN of PC. RESULTS: Three hundred forty-three patients (147 women; median age, 56 years) had MRI stages cT3cd, n = 170; cT4a, n = 40; and cT4b, n = 133. Median follow-up among survivors was 27 months (0.36-70 months). For M1 patients, OS differed only by cT stage (2-year OS: cT3 88.1%, cT4a 79.1%, cT4b 64.7%, p = 0.045). For M0 patients, OS and RFS differed only by pathological (p)T stage. We observed a statistically significant difference in the cumulative incidence of PC by cT stage (2-year CUIN: cT3 3.2%, cT4a 8.5%, cT4b 1.6%, p = 0.01), but not by pT stage. Seventy-nine patients (23%) presented with metastatic disease (M1), eight with PC (2.3%). Overall, eight patients presented with PC (cT4a: n = 4, other stages: n = 4) and 22 developed PC (cT4a: n = 5, other stages: n = 17). CONCLUSIONS: PC is uncommon in rectal cancer. MRI-based T stage exhibited an overall association with the cumulative incidence of PC, and descriptively, cT4a stage appears to have the highest CUIN. KEY POINTS: • In a retrospective study of 343 patients with rectal cancer undergoing baseline MRI and clinical follow-up, we found that peritoneal carcinomatosis was rare. • We observed a significant overall association between PC at presentation and cT stage that appeared to be driven by the higher proportion of cT4a patients presenting with PC. • Among patients that did not present with PC, we observed a significant overall association between time to PC and cT stage that may be driven by the higher cumulative incidence of PC in cT4a patients.

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.

We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of 0.93±0.06 (0.89±0.01) and 0.89±0.01 (0.81±0.02) was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.

Multi-practice survey on MR imaging practice patterns in rectal cancer in the United States.

PURPOSE: To investigate practice patterns related to MR technique and structured reporting for MRI of rectal cancer at academic centers and private practice groups in the United States. METHODS AND MATERIALS: A survey developed by active members of the Society of Abdominal Radiology Rectal and Anal Cancer Disease Focus Panel was sent to 100 private practice and 189 academic radiology groups. The survey asked targeted questions about practice demographics and utilization, technical MR parameters and reporting practices related to MRI of rectal cancer. The results were analyzed using software in an online survey program. RESULTS: The survey received 47 unique responses from academic (37/47, 78.7%) and private practice (10/47, 21.3%) groups. Most practices report using rectal MR for staging newly diagnosed rectal cancer always (29/47, 61.7%), and less than half always do so for re-staging after neoadjuvant chemoradiation (20/47, 42.6%). Most groups perform between 1 to 5 rectal MR studies for baseline staging per week (32/47, 68.1%) and most groups perform 0 to 2 MR studies for re-staging per week (27/47, 57.4%). Regarding patient preparation and MR technical parameters, there is variability, but a few key points include most practices (27/47, 57.4%) acquire axial T2-weighted images at a slice thickness of 3 mm or less, axial T2-weighted images with 2D acquisition (30/47, 63.8%), a T2-weighted axial oblique sequence through the tumor (43/47, 91.5%), and T2-weighted images without fat suppression (37/47, 78.7%). Equal numbers of groups report using a maximal b-value less than 1000 s/mm2 (19/43, 44.2%) and 1000 s/mm2 or greater (19/43, 44.2%); the rest were unsure. A substantial portion of respondents do not use intravenous contrast (13/47, 27.7%). Most believe that structured report templates contribute to uniformity of reporting practices (39/47, 83.0%), though there is considerable heterogeneity in usage and included elements. CONCLUSION: There is considerable technical heterogeneity among respondents' answers and reporting practices in MR for rectal cancer, and most of the groups report reading only a modest number of studies per week. Our findings suggest there may be room for improvement in terms of radiologist education for performance and standardization of clinical practice for MR imaging of rectal cancer.

MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer.

OBJECTIVE: To interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with locally advanced rectal cancer. METHODS: This retrospective study included patients who underwent neoadjuvant chemoradiotherapy for locally advanced rectal cancer from 2009 to 2015. Three radiologists independently segmented mesorectal fat on baseline T2-weighted axial MRI. Radiomics features were extracted from segmented volumes and calculated using CERR software, with adaptive synthetic sampling being employed to combat large class imbalances. Outcome variables included pathologic complete response (pCR), local recurrence, distant recurrence, clinical T-category (cT), post-treatment T category (ypT), and post-treatment N category (ypN). A maximum of eight most important features were selected for model development using support vector machines and fivefold cross-validation to predict each outcome parameter via elastic net regularization. Diagnostic metrics of the final models were calculated, including sensitivity, specificity, PPV, NPV, accuracy, and AUC. RESULTS: The study included 236 patients (54 ± 12 years, 135 men). The AUC, sensitivity, specificity, PPV, NPV, and accuracy for each clinical outcome were as follows: for pCR, 0.89, 78.0%, 85.1%, 52.5%, 94.9%, 83.9%; for local recurrence, 0.79, 68.3%, 80.7%, 46.7%, 91.2%, 78.3%; for distant recurrence, 0.87, 80.0%, 88.4%, 58.3%, 95.6%, 87.0%; for cT, 0.80, 85.8%, 56.5%, 89.1%, 49.1%, 80.1%; for ypN, 0.74, 65.0%, 80.1%, 52.7%, 87.0%, 76.3%; and for ypT, 0.86, 81.3%, 84.2%, 96.4%, 46.4%, 81.8%. CONCLUSION: Radiomics features of mesorectal fat can predict pathological complete response and local and distant recurrence, as well as post-treatment T and N categories. KEY POINTS: • Mesorectal fat contains important prognostic information in patients with locally advanced rectal cancer (LARC). • Radiomics features of mesorectal fat were significantly different between those who achieved complete vs incomplete pathologic response (accuracy 83.9%, 95% CI: 78.6-88.4%). • Radiomics features of mesorectal fat were significantly different between those who did vs did not develop local or distant recurrence (accuracy 78.3%, 95% CI: 72.0-83.7% and 87.0%, 95% CI: 81.6-91.2% respectively).

MRI for Rectal Cancer: Staging, mrCRM, EMVI, Lymph Node Staging and Post-Treatment Response.

Rectal cancer is a relatively common malignancy in the United States. Magnetic resonance imaging (MRI) of rectal cancer has evolved tremendously in recent years, and has become a key component of baseline staging and treatment planning. In addition to assessing the primary tumor and locoregional lymph nodes, rectal MRI can be used to help with risk stratification by identifying high-risk features such as extramural vascular invasion and can assess treatment response for patients receiving neoadjuvant therapy. As the practice of rectal MRI continues to expand further into academic centers and private practices, standard MRI protocols, and reporting are critical. In addition, it is imperative that the radiologists reading these cases work closely with surgeons, medical oncologists, radiation oncologists, and pathologists to ensure we are providing the best possible care to patients. This review aims to provide a broad overview of the role of MRI for rectal cancer.