Predicting Positive Radial Margin on Restaging MRI of Patients with Low Rectal Cancer: Can We Do Better?

Objective The aim of this study was to characterize the tissue involving the margin and study if this information will affect margin prediction on restaging magnetic resonance imaging (MRI) in low rectal adenocarcinoma (LRC) patients treated with neoadjuvant long-course chemoradiotherapy (LCCRT). Methods In this retrospective study of nonmetastatic LRC (distal margin <5 cm from the anal verge) treated with LCCRT followed by surgery, a radiologist blinded to outcome reread the restaging MRI and documented if the radial margin was involved by tumor, fibrosis, or mucin reaction using T2 high-resolution (HR) and diffusion-weighted imaging (DWI). The diagnostic performance of tumor-involving margin on restaging MRI was assessed using surgical histopathology as a reference. Interobserver agreement between three independent radiologists was assessed in a subset. Results We included 133 patients (80 males and 53 females) with a mean (range) age of 44.7 (21-86) years and 82% of them had well or moderately differentiated adenocarcinoma. Baseline MRI showed T3 ( n = 58) or T4 ( n = 60) disease in 89% of the patients. The pathological margin was positive in 21% ( n = 28) cases. In restaging MRI, the circumferential resection margin (CRM) ≤1 mm in 75.1% ( n = 100) cases and MRI predicted tumor, fibrosis, and mucin reaction at the margin in 60, 34, and 6%, respectively, and histopathology showed tumor cells in 33, 14.7, and 16.6% of them, respectively. LRC with tumor-involving margin and bad response (MR tumor regression grade [mr-TRG] 3-5) on restaging MRI had fourfold increased risk of positive pathological circumferential resection margin (pCRM). There was moderate and fair inter-reader agreement for the tissue type involving the CRM ( κ = 0.471) and mr-TRG ( κ = 0.266), p < 0.05. The use of both distance criteria and tumor-involving margins improved the diagnostic accuracy for margin prediction from 39 to 66% on restaging MRI. Conclusions Margin prediction on restaging MRI can be improved by characterizing the tissue type involving the margin in low rectal cancer patients. The inter-reader agreement was moderate for determining the tissue type.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

OBJECTIVES: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

PURPOSE: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

PURPOSE: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. METHODS: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. RESULTS: Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [ P < 0.0001]). CONCLUSIONS: Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

MDT stage migration in MRI of significant rectal polyps and early rectal cancers.

PURPOSE: To audit the diagnostic accuracy of MRI for staging early and polyp rectal cancers with the purpose of identifying scope for service improvement. METHODS: This is an IRB approved retrospective study of patients who underwent staging MRI for rectal growths followed by upfront TME type surgery or local excision without neoadjuvant therapy between 2018 and 2021. MR-T-stage was compared with surgical histopathology. The degree of stage migration in the multidisciplinary team meetings (MDT) was assessed and training needs were identified. RESULTS: 53 patients (32 males) with a mean (SD) age of 56.7 (13.6) years with 54 rectal lesions and underwent trans-anal excision (n = 18) or upfront surgery (n = 35) were included. Pathology showed < / = pT1 stage in n = 18 and > / = pT2 stage in n = 36. Radio-pathological concordance rate was 38.9% and 74.1%, respectively, for primary reports and MDT reads, respectively, and during MDT, the rates improved by 44.5% and 30.5% for < / = pT1 and > / = pT2 stages ,respectively. The overall T-stage migration rate at MDT was 44.6% (25/54) and the migration rate was higher (61.1%) for < / = pT1 stage lesions. The best sensitivity, specificity, PPV, NPV and accuracy of MRI for T-staging was 83.3%, 91.6%, 83.3%, 91.6% and 88.8%, respectively. CONCLUSION: Radio-pathological correlation for MRI T-stage is excellent for MDT reads by experienced radiologists. MDT reads lead to significant down-staging of T-stage in polyp and early rectal cancer thereby improving radio-path correlation.

Atypical Metastases in the Abdomen and Pelvis From Biochemically Recurrent Prostate Cancer: 11C-Choline PET/CT With Multimodality Correlation.

PET with targeted radiotracers has become integral to mapping the location and burden of recurrent disease in patients with biochemical recurrence (BCR) of prostate cancer (PCa). PET with 11C-choline is part of the National Comprehensive Cancer Network and European Association of Urology guidelines for evaluation of BCR. With advances in PET technology, increasing use of targeted radiotracers, and improved survival of patients with BCR because of novel therapeutics, atypical sites of metastases are being increasingly encountered, challenging the conventional view that prostate cancer rarely metastasizes beyond bones or lymph nodes. The purpose of this article is to describe such atypical metastases in the abdomen and pelvis on 11C-choline PET (including metastases to the liver, pancreas, genital tract, urinary tract, peritoneum, abdominal wall, and perineural spread) and to present multimodality imaging features and relevant imaging pitfalls. Given atypical metastases' inconsistent relationship with the serum PSA level and the nonspecific presenting symptoms, atypical metastases are often first detected on imaging. Awareness of their imaging features is important because their detection affects clinical management, patient counseling, prognosis, and clinical trial eligibility. Such awareness is particularly critical because the role of radiologists in the imaging and management of BCR will continue to increase given the expanding regulatory approvals of other targeted and theranostic radiotracers.

PSMA as a Theranostic Target in Hepatocellular Carcinoma: Immunohistochemistry and 68 Ga-PSMA-11 PET Using Cyclotron-Produced 68 Ga.

Prostate-specific membrane antigen (PSMA) is a validated target for molecular diagnostics and targeted radionuclide therapy. Our purpose was to evaluate PSMA expression in hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and hepatic adenoma (HCA); investigate the genetic pathways in HCC associated with PSMA expression; and evaluate HCC detection rate with 68 Ga-PSMA-11 positron emission tomography (PET). In phase 1, PSMA immunohistochemistry (IHC) on HCC (n = 148), CCA (n = 111), and HCA (n = 78) was scored. In a subset (n = 30), messenger RNA (mRNA) data from the Cancer Genome Atlas HCC RNA sequencing were correlated with PSMA expression. In phase 2, 68 Ga-PSMA-11 PET was prospectively performed in patients with treatment-naïve HCC on a digital PET scanner using cyclotron-produced 68 Ga. Uptake was graded qualitatively and semi-quantitatively using standard metrics. On IHC, PSMA expression was significantly higher in HCC compared with CCA and HCA (P < 0.0001); 91% of HCCs (n = 134) expressed PSMA, which principally localized to tumor-associated neovasculature. Higher tumor grade was associated with PSMA expression (P = 0.012) but there was no association with tumor size (P = 0.14), fibrosis (P = 0.35), cirrhosis (P = 0.74), hepatitis B virus (P = 0.31), or hepatitis C virus (P = 0.15). Overall survival tended to be longer in patients without versus with PSMA expression (median overall survival: 4.2 vs. 1.9 years; P = 0.273). FGF14 (fibroblast growth factor 14) mRNA expression correlated positively (rho = 0.70; P = 1.70 × 10-5 ) and MAD1L1 (Mitotic spindle assembly checkpoint protein MAD1) correlated negatively with PSMA expression (rho = -0.753; P = 1.58 × 10-6 ). Of the 190 patients who met the eligibility criteria, 31 patients with 39 HCC lesions completed PET; 64% (n = 25) lesions had pronounced 68 Ga-PSMA-11 standardized uptake value: SUVmax (median [range] 9.2 [4.9-28.4]), SUVmean 4.7 (2.4-12.7), and tumor-to-liver background ratio 2 (1.1-11). Conclusion: Ex vivo expression of PSMA in neovasculature of HCC translates to marked tumor avidity on 68 Ga-PSMA-11 PET, which suggests that PSMA has the potential as a theranostic target in patients with HCC.

Learning to Generate Missing Pulse Sequence in MRI using Deep Convolution Neural Network Trained with Visual Turing Test.

Magnetic resonance imaging (MRI) is widely used in clinical applications due to its ability to acquire a wide variety of soft tissues using multiple pulse sequences. Each sequence provides information that generally complements the other. However, factors like an increase in scan time or contrast allergies impede imaging with numerous sequences. Synthesizing images of such non acquired sequences is a challenging proposition that can suffice for corrupted acquisition, fast reconstruction prior, super-resolution, etc. This manuscript employed a deep convolution neural network (CNN) to synthesize multiple missing pulse sequences of brain MRI with tumors. The CNN is an encoder-decoder-like network trained to minimize reconstruction mean square error (MSE) loss while maximizing the adversarial attack. It inflicts on a relativistic Visual Turing Test discriminator (rVTT). The approach is evaluated through experiments performed with the Brats2018 dataset, quantitative metrics viz. MSE, Structural Similarity Measure (SSIM), and Peak Signal to Noise Ratio (PSNR). The Radiologist and MR physicist performed the Turing test with 76% accuracy, demonstrating our approach's performance superiority over the prior art. We can synthesize MR images of missing pulse sequences at an inference cost of 350.71 GFlops/voxel through this approach.

Radiology Reporting Errors: Learning from Report Addenda.

Background The addition of new information to a completed radiology report in the form of an "addendum" conveys a variety of information, ranging from less significant typographical errors to serious omissions and misinterpretations. Understanding the reasons for errors and their clinical implications will lead to better clinical governance and radiology practice. Aims This article assesses the common reasons which lead to addenda generation to completed reports and their clinical implications. Subjects and Methods Retrospective study was conducted by reviewing addenda to computed tomography (CT), ultrasound, and magnetic resonance imaging reports between January 2018 to June 2018, to note the frequency and classification of report addenda. Results Rate of addenda generation was 1.1% ( n = 1,076) among the 97,003 approved cross-sectional radiology reports. Errors contributed to 71.2% ( n = 767) of addenda, most commonly communication (29.3%, n = 316) and observational errors (20.8%, n = 224), and 28.7% were nonerrors aimed at providing additional clinically relevant information. Majority of the addenda (82.3%, n = 886) did not have a significant clinical impact. CT and ultrasound reports accounted for 36.9% ( n = 398) and 35.2% ( n = 379) share, respectively. A time gap of 1 to 7 days was noted for 46.8% ( n = 504) addenda and 37.6% ( n = 405) were issued in less than a day. Radiologists with more than 6-year experience created majority (1.5%, n = 456) of addenda. Those which were added to reports generated during emergency hours contributed to 23.2% ( n = 250) of the addenda. Conclusion The study has identified the prevalence of report addenda in a radiology practice involving picture archiving and communication system in a tertiary care center in India. The etiology included both errors and non-errors. Results of this audit were used to generate a checklist and put protocols that will help decrease serious radiology misses and common errors.

Diffuse Infiltrative Non-mass-like Brain Parenchymal Lesions on MRI: Differentiating Lymphomatosis Cerebri from its Mimics.

OBJECTIVES: Diffuse infiltrative "non-mass-like" parenchymal lesions on MRI brain are a known presentation of an aggressive condition called lymphomatosis cerebri (LC) but are often misdiagnosed due to its non-specific clinical and imaging findings. We aim to identify clues to differentiate lymphomatosis from its less aggressive mimics based on imaging features. MATERIAL AND METHODS: MRI brain studies showing diffuse infiltrative "non-mass-like" parenchymal lesions between January 2013 and March 2020 were retrospectively identified and read for lesion location, signal characteristics, and enhancement pattern by two radiologists. Additional findings on MRI spine and whole-body fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) were recorded wherever available. The clinical diagnosis, patient demographics, symptoms, laboratory and histopathology results, treatment details, and follow-up details were also noted. RESULTS: Of the 67 patients, 28 (41.7%) were diagnosed with lymphomatosis. The remaining 39 (13.4%) patients were classified as non-lymphomas (infective, vasculitis, and inflammatory conditions). Diffusion restriction on MRI (20/67, P = 0.007) and increased regional activity on FDG PET-CT (12/31, P = 0.017) were the two imaging parameters found to significantly favor lymphomatosis over other conditions, whereas the presence of microhemorrhages on susceptibility-weighted imaging was significantly associated with vasculitis (P = 0.002). Rapid clinical or imaging deterioration on a short trial of steroids (P = 0.00) was the only relevant clinical factor to raise an early alarm of lymphomatosis. Positive serological markers and non-central nervous system systemic diseases were associated with non-lymphomatous diseases. CONCLUSION: LC and its less aggressive mimics can be differentiated on diffusion-weighted imaging-MRI and PET-CT when read in conjunction with rapid progression of clinical features, serological workup, and systemic evaluation.

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

OBJECTIVE: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs. METHODS: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3). RESULTS: We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively]. CONCLUSION: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.

Biopsy with Side-Cutting Coaxial Needle-Knowing the "Cutting Length" and "Throw Length".

Image-guided Trucut biopsy is a well-established procedure. The length of the side notch in the stylet is the "cutting length," which entraps the tissue sample and contributes to the yield. The total distance by which the inner stylet protrudes from the outer cannula with the cutting notch open is the "throw length." It is inevitably longer than the cutting length does not add to the yield of the sample, but potentially to the complication of the procedure. The authors highlight the importance of knowing this distinction to minimize complications during the procedure.

MR Imaging in Neurocritical Care.

How to cite this article: Patra A, Janu A, Sahu A. MR Imaging in Neurocritical Care. Indian J Crit Care Med 2019;23(Suppl 2):S104-S114.