Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

Background: In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. Methods: This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Results: Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Conclusions: Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Watch & wait - Post neoadjuvant imaging for rectal cancer.

Rectal cancer management has evolved over the past decade with the emergence of total neoadjuvant therapy (TNT). For select patients who achieve a clinical complete response following TNT, organ preservation by means of the watch-and-wait (WW) strategy is an increasingly adopted alternative that preserves rectal function and quality of life without compromising oncologic outcomes. Recently, published 5-year results from the OPRA trial demonstrated that organ preservation can be achieved in approximately half of patients managed with the WW strategy, with most local regrowth events occurring within two years. Considering the potential for local regrowth, the implementation of the WW strategy mandates rigorous clinical and radiographic surveillance. Magnetic resonance imaging (MRI) serves as the conventional imaging modality for local staging and surveillance of rectal cancer given its excellent soft-tissue resolution. This review will discuss the current evidence for the WW strategy and the role of restaging rectal MRI in determining patient eligibility for this strategy. Restaging rectal MRI acquisition parameters and treatment response assessment, including important factors to assess, pitfalls, and classification systems, will be discussed in the context of the WW strategy.

Neurologic pathologies of the vertebral spine.

At some institutions, musculoskeletal and general radiologists rather than neuroradiologists are responsible for reading magnetic resonance imaging (MRI) of the spine. However, neurological findings, especially intrathecal ones, can be challenging. Intrathecal neurological findings in the spine can be classified by location (epidural, intradural extramedullary, and intramedullary) or etiology (tumor, infection, inflammatory, congenital). In this paper, we provide a succinct review of the intrathecal neurological findings that can be seen on MRI of the spine, primarily by location and secondarily by etiology, in order that this may serve as a helpful guide for musculoskeletal and general radiologists when encountering intrathecal neurological pathologies.

Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.

PURPOSE: To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI. METHODS: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. RESULTS: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts. CONCLUSION: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.

Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

PURPOSE: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

Convolutional network denoising for acceleration of multi-shot diffusion MRI.

Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase.

Radiation and Immune Checkpoint Inhibitors: Combination Therapy for Treatment of Hepatocellular Carcinoma.

The liver tumor immune microenvironment has been thought to possess a critical role in the development and progression of hepatocellular carcinoma (HCC). Despite the approval of immune checkpoint inhibitors (ICIs), such as programmed cell death receptor 1 (PD-1)/programmed cell death ligand 1 (PD-L1) and cytotoxic T lymphocyte associated protein 4 (CTLA-4) inhibitors, for several types of cancers, including HCC, liver metastases have shown evidence of resistance or poor response to immunotherapies. Radiation therapy (RT) has displayed evidence of immunosuppressive effects through the upregulation of immune checkpoint molecules post-treatment. However, it was revealed that the limitations of ICIs can be overcome through the use of RT, as it can reshape the liver immune microenvironment. Moreover, ICIs are able to overcome the RT-induced inhibitory signals, effectively restoring anti-tumor activity. Owing to the synergetic effect believed to arise from the combination of ICIs with RT, several clinical trials are currently ongoing to assess the efficacy and safety of this treatment for patients with HCC.

Follow-up imaging of anal cancer after treatment.

Anal cancer treatment response assessment can be challenging with both magnetic resonance imaging (MRI) and clinical evaluation considered essential. MRI, in particular, has shown to be useful for the assessment of treatment response, the detection of recurrent disease in follow up and surveillance, and the evaluation of possible post-treatment complications as well as complications from the tumor itself. In this review, we focus on the role of imaging, mainly MRI, in anal cancer treatment response assessment. We also describe the treatment complications that can occur, and the imaging findings associated with those complications.

Beyond squamous cell carcinoma: MRI appearance of uncommon anal neoplasms and mimickers.

Anal cancer is an uncommon malignancy. In addition to squamous cell carcinoma, there are a variety of other less common malignancies and benign pathologies that may afflict the anal canal, with which abdominal radiologists should be familiar. Abdominal radiologists should be familiar with the imaging features that can help distinguish different rare anal tumors beyond squamous cell carcinoma and that can aid in diagnosis therefore help steer management. This review discusses these uncommon pathologies with a focus on their imaging appearance, management, and prognosis.

Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network.

This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1-L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer.

Non-Surgical Locoregional Therapies Alone or in Combination with Systemic Therapy in Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, representing the third-leading cause of cancer-related deaths worldwide. Curative intent treatment options for patients with HCC include liver transplantation, resection and ablation of small lesions. Other potentially curative therapies include cryoablation, microwave ablation and percutaneous alcohol injection. For locally advanced disease, different arterially directed therapies including transarterial chemoembolization and selective internal radiation therapy, plus external beam radiation including three-dimensional conformal radiation therapy, intensity-modulated radiation therapy, stereotactic body radiation therapy and proton beam therapy, are available or studied. Systemic therapies based on checkpoint inhibitors and tyrosine kinase inhibitors are available for the management of metastatic HCC and sometimes for locally advanced disease. Combinations of locoregional therapies with systemic drugs are currently the subject of several clinical trials.

Mucinous Degeneration on MRI After Neoadjuvant Therapy in Patients With Rectal Adenocarcinoma: Frequency and Association With Clinical Outcomes.

BACKGROUND. Patients with nonmucinous rectal adenocarcinoma may develop mucinous changes after neoadjuvant chemoradiotherapy, which are described as mucinous degeneration. The finding's significance in earlier studies has varied. OBJECTIVE. The purpose of this study was to assess the frequency of mucinous degeneration on MRI after neoadjuvant therapy for rectal adenocarcinoma and to compare outcomes among patients with nonmucinous tumor, mucinous tumor, and mucinous degeneration on MRI. METHODS. This retrospective study included 201 patients (83 women, 118 men; mean age, 61.8 ± 2.2 [SD] years) with rectal adenocarcinoma who underwent neoadjuvant chemoradiotherapy followed by total mesorectal excision from October 2011 to November 2015, underwent baseline and restaging rectal MRI examinations, and had at least 2 years of follow-up. Two radiologists independently evaluated MRI examinations for mucin content, which was defined as T2 hyperintensity in the tumor or tumor bed, and resolved differences by consensus. Patients were classified into three groups on the basis of mucin status: those with nonmucinous tumor (≤ 50% mucin content on baseline and restaging examinations), those with mucinous tumor (> 50% mucin content on baseline and restaging examinations), and those with mucinous degeneration (≤ 50% mucin content on baseline examination and > 50% content on restaging examination). The three groups were compared. RESULTS. Interreader agreement for mucin content, expressed as a kappa coefficient, was 0.893 on baseline MRI and 0.890 on restaging MRI. Of the 201 patients, 156 (77.6%) had nonmucinous tumor, 34 (16.9%) had mucinous tumor, and 11 (5.5%) had mucinous degeneration. Mucin status was not significantly associated with complete pathologic response (p = .41) or local or distant recurrence (both p > .05). The death rate during follow-up was not significantly different (p = .21) between patients with nonmucinous tumor (23.1%), those with mucinous tumor (29.4%), and those with mucinous degeneration (9.1%). In adjusted Cox regression analysis, with mucinous degeneration used as reference, the HR for the overall survival rate for the mucinous tumor group was 4.7 (95% CI, 0.6-38.3; p = .14), and that for the nonmucinous tumor group was 8.0 (95% CI, 0.9-59.9; p = .06). On histopathologic assessment, all 11 patients with mucinous degeneration showed acellular mucin, yet 10 of 11 patients showed viable tumor (i.e., in nonmucinous portions of the tumors). CONCLUSION. Mucinous degeneration on MRI is not significantly associated with pathologic complete response, recurrence, or survival. CLINICAL IMPACT. Mucinous degeneration on MRI is uncommon and should not be deemed an indicator of pathologic complete response.

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.

Rectal MRI Interpretation After Neoadjuvant Therapy.

In recent years, several key advances in the management of locally advanced rectal cancer have been made, including the implementation of total mesorectal excision as the standard surgical approach; use of neoadjuvant chemoradiotherapy in selected patients with a high risk of local recurrence, and finally, adoption of organ preservation strategies, through either local excision or nonoperative management in selected patients with clinical complete response following neoadjuvant chemoradiotherapy. This review aims to shed light on the role of rectal MRI in the assessment of treatment response after neoadjuvant therapy, which is especially important given the growing feasibility of nonoperative management. First, an overview of current neoadjuvant therapies and response assessment based on digital rectal examination, endoscopy, and MRI will be provided. Second, the use of a high-quality restaging rectal MRI protocol will be presented. Third, a step-by-step approach to assessing treatment response on restaging rectal MRI following neoadjuvant treatment will be outlined, acknowledging challenges faced by radiologists during MRI interpretation. Finally, research related to response assessment will be discussed. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.

Local-Regional Treatment of Hepatocellular Carcinoma: A Primer for Radiologists.

The treatment planning for patients with hepatocellular carcinoma (HCC) relies predominantly on tumor burden, clinical performance, and liver function test results. Curative treatments such as resection, liver transplantation, and ablative therapies of small lesions should be considered for all patients with HCC. However, many patients are ineligible for these treatments owing to advanced disease stage and comorbidities. Despite efforts to increase screening, early-stage HCC remains difficult to diagnose, which decreases the possibility of curative therapies. In this context, local-regional treatment of HCC is accepted as a form of curative therapy in selected patients with early-stage disease, as a therapeutic option in patients who are not eligible to undergo curative therapies, as a downstaging approach to decrease tumor size toward meeting the criteria for liver transplantation, and as a bridging therapy to avoid tumor growth while the patient is on the waiting list for liver transplantation. The authors review the indications, types, mechanism of action, and possible complications of local-regional treatment, as well as the expected postprocedural imaging features of HCC. Furthermore, they discuss the role of imaging in pre- and postprocedural settings, provide guidance on how to assess treatment response, and review the current limitations of imaging assessment. Finally, the authors summarize the potential future directions with imaging tools that may add value to contemporary practice at response assessment and imaging biomarkers for patient selection, treatment response, and prognosis. ©RSNA, 2022.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

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.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma.

OBJECTIVES: To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. METHODS: The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV). RESULTS: There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%). CONCLUSIONS: MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies. KEY POINTS: • MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

A primer on texture analysis in abdominal radiology.

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.