RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial

BACKGROUND

Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

OBJECTIVE

To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

DESIGN, SETTING, AND PARTICIPANTS

Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models.

RESULTS AND LIMITATIONS

The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm.

CONCLUSIONS

We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care.

PATIENT SUMMARY

This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer

Predictive Model for Hormone Therapy in Prostate CancerDigital pathology images and clinical data from pretreatment prostate tissue were used to generate a predictive model to determine patients who would benefit from androgen deprivation therapy (ADT). In model-positive patients, ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer

Background

Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use.

Methods

Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis. After the model was locked, validation was performed on NRG/RTOG 9408 (n = 1,594) that randomized men to radiotherapy +/- 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and predictive model and within predictive model positive and negative subgroup treatment effects.

Results

In the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis (subdistribution hazard ratio [sHR] = 0.64, 95%CI [0.45-0.90], p = 0.01). The predictive model-treatment interaction was significant (p-interaction = 0.01). In predictive model positive patients (n = 543, 34%), ADT significantly reduced the risk of distant metastasis compared to radiotherapy alone (sHR = 0.34, 95%CI [0.19-0.63], p < 0.001). There were no significant differences between treatment arms in the predictive model negative subgroup (n = 1,051, 66%; sHR = 0.92, 95%CI [0.59-1.43], p = 0.71).

Conclusions

Our data, derived and validated from completed randomized phase III trials, show that an AI-based predictive model was able to identify prostate cancer patients, with predominately intermediate-risk disease, who are likely to benefit from short-term ADT.

Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models

Diabetic retinopathy (DR) is a major cause of vision impairment in diabetic patients worldwide. Due to its prevalence, early clinical diagnosis is essential to improve treatment management of DR patients. Despite recent demonstration of successful machine learning (ML) models for automated DR detection, there is a significant clinical need for robust models that can be trained with smaller cohorts of dataset and still perform with high diagnostic accuracy in independent clinical datasets (i.e., high model generalizability). Towards this need, we have developed a self-supervised contrastive learning (CL) based pipeline for classification of referable vs non-referable DR. Self-supervised CL based pretraining allows enhanced data representation, therefore, the development of robust and generalized deep learning (DL) models, even with small, labeled datasets. We have integrated a neural style transfer (NST) augmentation in the CL pipeline to produce models with better representations and initializations for the detection of DR in color fundus images. We compare our CL pretrained model performance with two state of the art baseline models pretrained with Imagenet weights. We further investigate the model performance with reduced labeled training data (down to 10 percent) to test the robustness of the model when trained with small, labeled datasets. The model is trained and validated on the EyePACS dataset and tested independently on clinical datasets from the University of Illinois, Chicago (UIC). Compared to baseline models, our CL pretrained FundusNet model had higher area under the receiver operating characteristics (ROC) curve (AUC) (CI) values (0.91 (0.898 to 0.930) vs 0.80 (0.783 to 0.820) and 0.83 (0.801 to 0.853) on UIC data). At 10 percent labeled training data, the FundusNet AUC was 0.81 (0.78 to 0.84) vs 0.58 (0.56 to 0.64) and 0.63 (0.60 to 0.66) in baseline models, when tested on the UIC dataset. CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.

Patient-level data meta-analysis of a multi-modal artificial intelligence (MMAI) prognostic biomarker in high-risk prostate cancer: Results from six NRG/RTOG phase III randomized trials

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Background: Recently, an MMAI prognostic biomarker, ArteraAI Prostate, was trained and validated in localized prostate cancer to more accurately risk stratify patients for multiple endpoints compared to NCCN risk groups (Esteva et al., 2022). Prognostication within an NCCN risk group remains clinically important given the multiple treatment decisions required within each risk group (e.g., radiotherapy dose or hormone therapy use). Herein, we validated the MMAI biomarker in high-risk prostate cancer where an increasing number of therapeutic decisions is required. Methods: This study leveraged histopathology image and clinical data from patients with at least one high-risk feature (HRF; cT3-cT4, Gleason 8-10, PSA > 20 ng/mL, primary Gleason pattern 5) from six NRG/RTOG phase III randomized trials (n=1,088). Patients from two trials not part of the initial MMAI biomarker training/validation (RTOG 0521 [n=344] and 9902 [n=318]) and the MMAI validation cohort (RTOG 9202, 9408, 9413, and 9910 [n=426]) were included. Fine-Gray, cumulative incidence, and time dependent area under the curve (tdAUC) analyses were performed for time to distant metastasis (DM) and prostate cancer-specific mortality (PCSM) for standard clinicopathologic variables (age, PSA, Gleason score, T-stage, number of HRFs) and the MMAI model, as a continuous score (per standard deviation increase) and categorically by quartile. Death from other causes were treated as competing risks. Results: The analyzed cohort had a median follow-up of 10.4 years. Median PSA was 21 ng/mL, 60% had Gleason 8-10 disease, 37% had cT3-T4 disease, and 20% were African American. On univariable analysis, the MMAI model was significantly associated with DM (subdistribution hazard ratio [sHR] 2.05, 95% CI 1.74-2.43, p<0.001) and PCSM (sHR 2.04, 95% CI 1.73-2.42, <0.001). On multivariable analysis, the MMAI model, adjusting for either age, PSA, Gleason score, T-stage, or number of HRFs, was the only variable significantly associated with DM. TdAUC was highest for the MMAI biomarker for both 5-year DM (0.71), compared to PSA (0.56), Gleason score (0.61), T-stage (0.63), or number of HRFs (0.64), and for 5-year PCSM (0.75), compared to clinicopathologic variables (range 0.53-0.63). The estimated 10-year DM and 15-year PCSM rates for MMAI quartile 1 vs 4 were 8% vs 31% and 8% vs 34%, respectively. Conclusions: Our novel MMAI prognostic biomarker was successfully validated across six phase III randomized trials with long-term follow-up to be independently prognostic over standard clinical and pathologic variables for men with high-risk prostate cancer. Despite all patients having high-risk disease, the MMAI biomarker identified those with highly variable risks for DM and PCSM. This tool can help enable personalized, shared decision making for patients and providers.

Regression of venous thrombus after trans-sphenoidal hypophysectomy for pituitary-dependent hyperadrenocorticism in a dog

An 8.0-kg 8-year-old male dachshund was presented for surgical treatment of suspected pituitary-dependent hyperadrenocorticism with portal vein thrombosis. Advanced diagnostic imaging revealed a thrombus in the splenic and portal veins. For the portal vein thrombus, CT angiography showed an enhanced timing delay in the lateral right and caudate liver lobes. Blood tests showed a marked increase in the liver panel, including total bile acid. Brain MRI revealed a pituitary mass, suggesting pituitary-dependent hyperadrenocorticism. The mass was completely resected. The preoperative antithrombotic therapy of rivaroxaban (0.66 mg/kg, PO, once per day) and clopidogrel sulphate (1.66 mg/kg, PO, once per day) was continued postoperatively. Six months after resection of the pituitary mass, the thrombus had disappeared. Further studies are required to prove a causal association between the disappearance of the thrombus and the treatments provided.

Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System Using US Cine-Clip Images

PURPOSE

To develop a deep learning-based risk stratification system for thyroid nodules using US cine images.

MATERIALS AND METHODS

In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS)-structured radiology reports were evaluated. A deep learning-based system that exploits the cine images obtained during three-dimensional volumetric thyroid scans and outputs malignancy risk was developed and compared, using fivefold cross-validation, against a two-dimensional (2D) deep learning-based model (Static-2DCNN), a radiomics-based model using cine images (Cine-Radiomics), and the ACR TI-RADS level, with histopathologic diagnosis as ground truth. The system was used to revise the ACR TI-RADS recommendation, and its diagnostic performance was compared against the original ACR TI-RADS.

RESULTS

The system achieved higher average area under the receiver operating characteristic curve (AUC, 0.88) than Static-2DCNN (0.72, P = .03) and tended toward higher average AUC than Cine-Radiomics (0.78, P = .16) and ACR TI-RADS level (0.80, P = .21). The system downgraded recommendations for 92 benign and two malignant nodules and upgraded none. The revised recommendation achieved higher specificity (139 of 175, 79.4%) than the original ACR TI-RADS (47 of 175, 26.9%; P < .001), with no difference in sensitivity (12 of 17, 71% and 14 of 17, 82%, respectively; P = .63).

CONCLUSION

The risk stratification system using US cine images had higher diagnostic performance than prior models and improved specificity of ACR TI-RADS when used to revise ACR TI-RADS recommendation.Keywords: Neural Networks, US, Abdomen/GI, Head/Neck, Thyroid, Computer Applications-3D, Oncology, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Evaluation of reduction efficiencies of pepper mild mottle virus and human enteric viruses in full-scale drinking water treatment plants employing coagulation-sedimentation-rapid sand filtration or coagulation-microfiltration

Here, we evaluated the reduction efficiencies of indigenous pepper mild mottle virus (PMMoV, a potential surrogate for human enteric viruses to assess virus removal by coagulation-sedimentation-rapid sand filtration [CS-RSF] and coagulation-microfiltration [C-MF]) and representative human enteric viruses in four full-scale drinking water treatment plants that use CS-RSF (Plants A and B) or C-MF (Plants C and D). First, we developed a virus concentration method by using an electropositive filter and a tangential-flow ultrafiltration membrane to effectively concentrate and recover PMMoV from large volumes of water: the recovery rates of PMMoV were 100% when 100-L samples of PMMoV-spiked dechlorinated tap water were concentrated to 20 mL; even when spiked water volume was 2000 L, recovery rates of >30% were maintained. The concentrations of indigenous PMMoV in raw and treated water samples determined by using this method were always above the quantification limit of the real-time polymerase chain reaction assay. We therefore were able to determine its reduction ratios: 0.9-2.7-log10 in full-scale CS-RSF and 0.7-2.9-log10 in full-scale C-MF. The PMMoV reduction ratios in C-MF at Plant C (1.0 ± 0.3-log10) were lower than those in CS-RSF at Plants A (1.7 ± 0.5-log10) and B (1.4 ± 0.7-log10), despite the higher ability of MF for particle separation in comparison with RSF owing to the small pore size in MF. Lab-scale virus-spiking C-MF experiments that mimicked full-scale C-MF revealed that a low dosage of coagulant (polyaluminum chloride [PACl]) applied in C-MF, which is determined mainly from the viewpoint of preventing membrane fouling, probably led to the low reduction ratios of PMMoV in C-MF. This implies that high virus reduction ratios (>4-log10) achieved in previous lab-scale virus-spiking C-MF studies are not necessarily achieved in full-scale C-MF. The PMMoV reduction ratios in C-MF at Plant D (2.2 ± 0.6-log10) were higher than those at Plant C, despite similar coagulant dosages. In lab-scale C-MF, the PMMoV reduction ratios increased from 1-log10 (with PACl [basicity 1.5], as at Plant C) to 2-4-log10 (with high-basicity PACl [basicity 2.1], as at Plant D), suggesting that the use of high-basicity PACl probably resulted in higher reduction ratios of PMMoV at Plant D than at Plant C. Finally, we compared the reduction ratios of indigenous PMMoV and representative human enteric viruses in full-scale CS-RSF and C-MF. At Plant D, the concentrations of human norovirus genogroup II (HuNoV GII) in raw water were sometimes above the quantification limit; however, whether its reduction ratios in C-MF were higher than those of PMMoV could not be judged since reduction ratios were >1.4-log10 for HuNoV GII and 2.3-2.9-log10 for PMMoV. At Plant B, the concentrations of enteroviruses (EVs) and HuNoV GII in raw water were above the quantification limit on one occasion, and the reduction ratios of EVs (>1.2-log10) and HuNoV GII (>1.5-log10) in CS-RSF were higher than that of PMMoV (0.9-log10). This finding supports the usefulness of PMMoV as a potential surrogate for human enteric viruses to assess virus removal by CS-RSF.

Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing

Purpose

To automatically identify a cohort of patients with pancreatic cystic lesions (PCLs) and extract PCL measurements from historical CT and MRI reports using natural language processing (NLP) and a question answering system.

Materials and Methods

Institutional review board approval was obtained for this retrospective Health Insurance Portability and Accountability Act-compliant study, and the requirement to obtain informed consent was waived. A cohort of free-text CT and MRI reports generated between January 1991 and July 2019 that covered the pancreatic region were identified. A PCL identification model was developed by modifying a rule-based information extraction model; measurement extraction was performed using a state-of-the-art question answering system. The system's performance was evaluated against radiologists' annotations.

Results

For this study, 430 426 free-text radiology reports from 199 783 unique patients were identified. The NLP model for identifying PCL was applied to 1000 test samples. The interobserver agreement between the model and two radiologists was almost perfect (Fleiss κ = 0.951), and the false-positive rate and true-positive rate were 3.0% and 98.2%, respectively, against consensus of radiologists' annotations as ground truths. The overall accuracy and Lin concordance correlation coefficient for measurement extraction were 0.958 and 0.874, respectively, against radiologists' annotations as ground truths.

Conclusion

An NLP-based system was developed that identifies patients with PCLs and extracts measurements from a large single-institution archive of free-text radiology reports. This approach may prove valuable to study the natural history and potential risks of PCLs and can be applied to many other use cases.Keywords: Informatics, Abdomen/GI, Pancreas, Cysts, Computer Applications-General (Informatics), Named Entity Recognition Supplemental material is available for this article. © RSNA, 2022See also commentary by Horii in this issue.

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at https://github.com/rikiyay/style-transfer-for-digital-pathology.

Deep Learning: An Update for Radiologists

Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data. These networks have numerous applications in radiology, particularly in image classification, object detection, semantic segmentation, and instance segmentation. The authors provide an update on a recent primer on deep learning for radiologists, and they review terminology, data requirements, and recent trends in the design of CNNs; illustrate building blocks and architectures adapted to computer vision tasks, including generative architectures; and discuss training and validation, performance metrics, visualization, and future directions. Familiarity with the key concepts described will help radiologists understand advances of deep learning in medical imaging and facilitate clinical adoption of these techniques. Online supplemental material is available for this article. ^©RSNA, 2021.

Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data

PURPOSE

Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC).

METHODS

Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no recurrence, local recurrence, and distant recurrence, based on clinician notes, radiology reports, and pathology reports compared with manual curation. A second NLP model was trained and validated to identify sites of recurrence. We compared the ability of each NLP model to identify the presence, timing, and site of recurrence, when compared against manual chart review and International Classification of Diseases coding.

RESULTS

A total of 1,273 patients were included in the development and validation of the two models. The NLP model for recurrence detects distant recurrence with an area under the curve of 0.98 (95% CI, 0.96 to 0.99) and 0.95 (95% CI, 0.88 to 0.98) in breast and HCC cohorts, respectively. The mean accuracy of the NLP model for detecting any site of distant recurrence was 0.9 for breast cancer and 0.83 for HCC. The NLP model for recurrence identified a larger proportion of patients with distant recurrence in a breast cancer database (11.1%) compared with International Classification of Diseases coding (2.31%).

CONCLUSION

We developed two NLP models to identify distant cancer recurrence, timing of recurrence, and sites of recurrence based on unstructured electronic health record data. These models can be used to perform large-scale retrospective studies in oncology.

Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer

Triple-negative breast cancer, the poorest-prognosis breast cancer subtype, lacks clinically approved biomarkers for patient risk stratification and treatment management. Prior literature has shown that interrogation of the tumor-immune microenvironment may be a promising approach to fill these gaps. Recently developed high-dimensional tissue imaging technology, such as multiplexed ion beam imaging, provide spatial context to protein expression in the microenvironment, allowing in-depth characterization of cellular processes. We demonstrate that profiling the functional proteins involved in cell-to-cell interactions in the microenvironment can predict recurrence and overall survival. We highlight the immunological relevance of the immunoregulatory proteins PD-1, PD-L1, IDO, and Lag3 by tying interactions involving them to recurrence and survival. Multivariate analysis reveals that our methods provide additional prognostic information compared to clinical variables. In this work, we present a computational pipeline for the examination of the tumor-immune microenvironment using multiplexed ion beam imaging that produces interpretable results, and is generalizable to other cancer types.

Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade

PURPOSE

The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade.

METHODS

Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade.

RESULTS

Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance.

CONCLUSION

CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.

Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

BACKGROUND

Accurate segmentation of the coronary arteries with intravascular ultrasound (IVUS) is important to optimize coronary stent implantation. Recently, deep learning (DL) methods have been proposed to develop automatic IVUS segmentation. However, most of those have been limited to segmenting the lumen and vessel (i.e. lumen-intima and media-adventitia borders), not applied to segmenting stent dimension. Hence, this study aimed to develop a DL method for automatic IVUS segmentation of stent area in addition to lumen and vessel area.

METHODS

This study included a total of 45,449 images from 1576 IVUS pullback runs. The datasets were randomly split into training, validation, and test datasets (0.7:0.15:0.15). After developing the DL-based system to segment IVUS images using the training and validation datasets, we evaluated the performance through the independent test dataset.

RESULTS

The DL-based segmentation correlated well with the expert-analyzed segmentation with a mean intersection over union (± standard deviation) of 0.80 ± 0.20, correlation coefficient of 0.98 (95% confidence intervals: 0.98 to 0.98), 0.96 (0.95 to 0.96), and 0.96 (0.96 to 0.96) for lumen, vessel, and stent area, and the mean difference (± standard deviation) of 0.02 ± 0.57, -0.44 ± 1.56 and - 0.17 ± 0.74 mm² for lumen, vessel and stent area, respectively.

CONCLUSION

This automated DL-based IVUS segmentation of lumen, vessel and stent area showed an excellent agreement with manual segmentation by experts, supporting the feasibility of artificial intelligence-assisted IVUS assessment in patients undergoing coronary stent implantation.

Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study

PURPOSE

To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.

MATERIALS AND METHODS

Preoperative computed tomography and tumor marker information of 617 primary liver cancer patients were retrospectively collected to develop CNN models categorizing tumors into three categories: moderately differentiated HCC (mHCC), poorly differentiated HCC (pHCC), and ICC, where the histopathological diagnoses were considered as ground truths. The models processed manually cropped tumor with and without tumor marker information (two-input and one-input models, respectively). Overall accuracy was assessed using a held-out dataset (10%). Area under the curve, sensitivity, and specificity for differentiating ICC from HCCs (mHCC + pHCC), and pHCC from mHCC were also evaluated. We assessed two radiologists' performance without tumor marker information as references (overall accuracy, sensitivity, and specificity). The two-input model was compared with the one-input model and radiologists using permutation tests.

RESULTS

The overall accuracy was 0.61, 0.60, 0.55, 0.53 for the two-input model, one-input model, radiologist 1, and radiologist 2, respectively. For differentiating pHCC from mHCC, the two-input model showed significantly higher specificity than radiologist 1 (0.68 [95% confidence interval: 0.50-0.83] vs 0.45 [95% confidence interval: 0.27-0.63]; p = 0.04).

CONCLUSION

Our CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

Deep learning enables the automation of grading histological tissue engineered cartilage images for quality control standardization

OBJECTIVE

To automate the grading of histological images of engineered cartilage tissues using deep learning.

METHODS

Cartilaginous tissues were engineered from various cell sources. Safranin O and fast green stained histological images of the tissues were graded for chondrogenic quality according to the Modified Bern Score, which ranks images on a scale from zero to six according to the intensity of staining and cell morphology. The whole images were tiled, and the tiles were graded by two experts and grouped into four categories with the following grades: 0, 1-2, 3-4, and 5-6. Deep learning was used to train models to classify images into these histological score groups. Finally, the tile grades per donor were averaged. The root mean square errors (RMSEs) were calculated between each user and the model.

RESULTS

Transfer learning using a pretrained DenseNet model was selected. The RMSEs of the model predictions and 95% confidence intervals were 0.49 (0.37, 0.61) and 0.78 (0.57, 0.99) for each user, which was in the same range as the inter-user RMSE of 0.71 (0.51, 0.93).

CONCLUSION

Using supervised deep learning, we could automate the scoring of histological images of engineered cartilage and achieve results with errors comparable to inter-user error. Thus, the model could enable the automation and standardization of assessments currently used for experimental studies as well as release criteria that ensure the quality of manufactured clinical grafts and compliance with regulatory requirements.

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

BACKGROUND

Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients remain untested. A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential of a deep learning-based system for automated MSI prediction directly from haematoxylin and eosin (H&E)-stained whole-slide images (WSIs).

METHODS

Our deep learning model (MSINet) was developed using 100 H&E-stained WSIs (50 with microsatellite stability [MSS] and 50 with MSI) scanned at 40× magnification, each from a patient randomly selected in a class-balanced manner from the pool of 343 patients who underwent primary colorectal cancer resection at Stanford University Medical Center (Stanford, CA, USA; internal dataset) between Jan 1, 2015, and Dec 31, 2017. We internally validated the model on a holdout test set (15 H&E-stained WSIs from 15 patients; seven cases with MSS and eight with MSI) and externally validated the model on 484 H&E-stained WSIs (402 cases with MSS and 77 with MSI; 479 patients) from The Cancer Genome Atlas, containing WSIs scanned at 40× and 20× magnification. Performance was primarily evaluated using the sensitivity, specificity, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). We compared the model's performance with that of five gastrointestinal pathologists on a class-balanced, randomly selected subset of 40× magnification WSIs from the external dataset (20 with MSS and 20 with MSI).

FINDINGS

The MSINet model achieved an AUROC of 0·931 (95% CI 0·771-1·000) on the holdout test set from the internal dataset and 0·779 (0·720-0·838) on the external dataset. On the external dataset, using a sensitivity-weighted operating point, the model achieved an NPV of 93·7% (95% CI 90·3-96·2), sensitivity of 76·0% (64·8-85·1), and specificity of 66·6% (61·8-71·2). On the reader experiment (40 cases), the model achieved an AUROC of 0·865 (95% CI 0·735-0·995). The mean AUROC performance of the five pathologists was 0·605 (95% CI 0·453-0·757).

INTERPRETATION

Our deep learning model exceeded the performance of experienced gastrointestinal pathologists at predicting MSI on H&E-stained WSIs. Within the current universal MSI testing paradigm, such a model might contribute value as an automated screening tool to triage patients for confirmatory testing, potentially reducing the number of tested patients, thereby resulting in substantial test-related labour and cost savings.

FUNDING

Stanford Cancer Institute and Stanford Departments of Pathology and Biomedical Data Science.

A Horizon Scan of research priorities to inform policies aimed at reducing the harm of plastic pollution to biota

Plastic pollution in the oceans is a priority environmental issue. The recent increase in research on the topic, coupled with growing public awareness, has catalyzed policymakers around the world to identify and implement solutions that minimize the harm caused by plastic pollution. To aid and coordinate these efforts, we surveyed experts with scientific experience identified through their peer-reviewed publications. We asked experts about the most pressing research questions relating to how biota interact with plastic pollution that in turn can inform policy decisions and research agendas to best contribute to understanding and reducing the harm of plastic pollution to biota. We used a modified Horizon Scan method that first used a subgroup of experts to generate 46 research questions on aquatic biota and plastics, and then conducted an online survey of researchers globally to prioritize questions in terms of their importance to inform policy development. One hundred and fifteen experts from 29 countries ranked research questions in six themes. The questions were ranked by urgency, indicating which research should be addressed immediately, which can be addressed later, and which are of limited relevance to inform action on plastics as an environmental pollutant. We found that questions relating to the following four themes were the most commonly top-ranked research priorities: (i) sources, circulation and distribution of plastics, (ii) type of harm from plastics, (iii) detection of ingested plastics and the associated problems, and (iv) related economies and policy to ingested plastics. While there are many research questions on the topic of impacts of plastic pollution on biota that could be funded and investigated, our results focus collective priorities in terms of research that experts believe will inform effective policy and on-the-ground conservation.

Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction

RATIONALE AND OBJECTIVES

To evaluate the utility of a convolutional neural network (CNN) with an increased number of contracting and expanding paths of U-net for sparse-view CT reconstruction.

MATERIALS AND METHODS

This study used 60 anonymized chest CT cases from a public database called "The Cancer Imaging Archive". Eight thousand images from 40 cases were used for training. Eight hundred and 80 images from another 20 cases were used for quantitative and qualitative evaluation, respectively. Sparse-view CT images subsampled by a factor of 20 were simulated, and two CNNs were trained to create denoised images from the sparse-view CT. A CNN based on U-net with residual learning with four contracting and expanding paths (the preceding CNN) was compared with another CNN with eight contracting and expanding paths (the proposed CNN) both quantitatively (peak signal to noise ratio, structural similarity index), and qualitatively (the scores given by two radiologists for anatomical visibility, artifact and noise, and overall image quality) using the Wilcoxon signed-rank test. Nodule and emphysema appearance were also evaluated qualitatively.

RESULTS

The proposed CNN was significantly better than the preceding CNN both quantitatively and qualitatively (overall image quality interquartile range, 3.0-3.5 versus 1.0-1.0 reported from the preceding CNN; p < 0.001). However, only 2 of 22 cases used for emphysematous evaluation (2 CNNs for every 11 cases with emphysema) had an average score of ≥ 2 (on a 3 point scale).

CONCLUSION

Increasing contracting and expanding paths may be useful for sparse-view CT reconstruction with CNN. However, poor reproducibility of emphysema appearance should also be noted.

Abstract P4-10-33: Alteration of the tumor immune microenvironment signatures by nivolumab combined with radiation therapy for patients with metastatic breast cancer (Translational Research of the KBCRN-B-002 trial)

BACKGROUND: Advances in imuuno-oncology (IO), i.e., a programmed cell death protein 1 (PD-1) inhibitors are revolutionizing cancer treatment by offering hope for a cure. However, the benefit of IO agents in metastatic breast cancer (MBC) is still limited. For immunotherapy to be successful, it is essential to understand the tumor immune contexture. Tumor immune microenvironment (TIME) alterations, such as increased effector-cell composition, inflamed immune cells, and the activated effector immune cells create conditions that enhance immunotherapy in MBC. The abscopal effect induced by Radiation therapy (RT) is known as a modulator of TIME. Then, abscopal effect is considered to be a systemic anti-tumor immune response. In this study, we integrated time series multi-omics data to identify alteration of the TIME signatures with RT combined with N\nivolumab, a PD-1 inhibitor, using a machine learning method.

PATIENTS AND METHODS: This study was conducted as a translational research of the KBCRN-B-002 trial, which is a multicenter phase Ib/II study for evaluating the safety and efficacy of nivolumab in combination with RT in patients with HER2-negative MBC (Takada M et al. will make presentation the main results of the study. (UMIN: UMIN000026046; ClinicalTrials.gov: NCT03430479)). Twenty-nine patients were included in the translational analysis set. The multi-omics data included data from RNAseq, DNAseq, mass cytometry (CyTOF), multiple cytokines, human leukocyte antigen, radiomics, drug concentrations, tuberculin reaction using peripheral blood mononuclear cells (PBMCs), serum, plasma, imaging, and Formalin-Fixed Paraffin-Embedded (FFPE) samples. We collected time series data, which included data for the baseline, two weeks after starting therapy, four weeks after starting therapy, and the timing of progressive disease. For the integrated analysis, a unique machine learning method was developed.

RESULTS: Our integrated analysis identified the alteration of the tumor immune microenvironment signatures using nivolumab combined with RT for MBC. These results included the signature of the immune cell composition, effector immune cells, and immune suppressor cells, via single-cell analysis using CyTOF. The types of analysis are given in the table.

CONCLUSIONS: We identified candidates for TIME signatures for actionable hallmarks of RT combined with IO agent by our machine learning method.

Type of AnalysisSampleTime PointBaseline2 Weeks4 WeeksProgressive DiseaseRNAseqFFPE✓RNAseqPBMCs✓✓✓✓DNAseqFFPE✓CyTOFPBMCs✓✓✓✓CytokinesSerum✓✓✓✓Nivolumab ConcentrationSerum✓✓✓✓TILsFFPE✓HLAPBMCs✓Tuberculin ReactionSkin✓

Citation Format: Kosuke Kawaguchi, Masahiro Takada, Takeshi Kotake, Michio Yoshimura, Ryuji Uozumi, Masako Kataoka, Takahiko Koyama, Reitaro Tokumasu, Maya Honda, Rikiya Yamashita, Atsushi Yonezawa, Yuki Himoto, Natsuko Onishi, Hironori Kato, Hiroshi Yoshibayashi, Hirofumi Suwa, Wakako Tsuji, Hiroyasu Yamashiro, Tatsuki Kataoka, Hiroshi Ishiguro, Laxmi Parida, Satoshi Morita, Masakazu Toi. Alteration of the tumor immune microenvironment signatures by nivolumab combined with radiation therapy for patients with metastatic breast cancer (Translational Research of the KBCRN-B-002 trial) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-33.

Abstract P3-09-07: A multicenter phase Ib/II study for evaluating safety and efficacy of Nivolumab in combination with radiation therapy in patients with HER2-negative metastatic breast cancer (KBCRN-B-002 trial)

BACKGROUND: In metastatic breast cancer, Overall Response Rate (ORR) for checkpoint inhibitor monotherapy has been limited. Since Radiation Therapy (RT) stimulates anti-cancer immune responses, we evaluated safety and efficacy of a combinatorial therapy of RT with Nivolumab (a Programmed cell death protein 1 (PD-1) inhibitor) on HER2-negative metastatic breast cancer patients in a multicenter and non-randomized phase Ib/II study. PATIENTS AND METHODS: Patients with HER2-negative breast cancer, measurable metastasis (RECIST1.1) and at least one bone metastasis requiring radiation therapy were enrolled. The patients were divided into two cohorts, A and B. Cohort A constitutes estrogen and/or progesterone receptor-positive breast cancer patients that had 1 or 2 lines of endocrine therapy for the metastatic disease. Cohort B constitutes triple negative breast cancer patients that had two or more lines of chemotherapy for metastatic disease and anthracycline and taxane treatment for any disease setting. Single fraction RT of 8 Gy was delivered on day 0. Patients received 3 mg/kg Nivolumab intravenously every 2 weeks from day 1 until the disease progressed. Combined use of endocrine therapy of the physician’s choice was allowed in Cohort A. Primary endpoints were safety and ORR (RECIST1.1) in phase Ib and phase II, respectively. Secondary endpoints were Duration of Response (DOR), Disease Control Rate (DCR) and Progression-Free Survival (PFS). For translational research, we integrated time series multi-omics data that are correlated with Tumor Immune Microenvironment (TIME) to identify novel drug sensitivity-associated signatures using machine learning method. The multi-omics data included data from RNAseq, DNAseq, mass cytometry (CyTOF), multiple cytokines, human leukocyte antigen, radiomics, drug concentrations, tuberculin reaction using Peripheral Blood Mononuclear Cells (PBMCs), serum, plasma, imaging and Formalin-Fixed Paraffin-Embedded (FFPE) samples. RESULTS: 31 patients were enrolled from Jan 2017 to Nov 2018 and 29 patients (18 in Cohort A and 11 in Cohort B) were included in the full analysis set. While seven patients in Cohort A had received 1 or 2 lines of chemotherapy earlier for the metastatic disease, eight patients in Cohort B received three or more lines of chemotherapy. Dose limiting toxicities were not observed in phase Ib. In Cohort A, the ORR was 11.1% (90% with a Confidence Interval (CI) of 3.7-28.6). The best overall responses were: Partial Response (PR) in 2 patients (11.1%), Stable Disease (SD) in 11 patients (61.1%) and Progressive Disease (PD) in 5 patients (27.8%). In Cohort B, ORR was 0% as none of the patients responded to the treatment. The best overall responses were: SD in 4 patients (36.4%), PD in 6 patients (54.5%) and one patient (9.1%) was not evaluated (NE). While the DCR for Cohorts A and B were 72.2% (90% CI: 52.9-85.8) and 36.4% (90% CI: 17.5-60.6), respectively, the median PFS were 4 months (95% CI: 2.1-5.5 months) and 2 months (95% CI: 1.2-4.0 months), respectively. Common toxicities were mild; including 3 patients in Cohort A (16.7%) and 2 patients in Cohort B (18.2%) that experienced grade 3 or 4 toxicity. There were no deaths due to adverse effects. Furthermore, our integrated analysis identified novel candidates for drug sensitivity-associated signatures. CONCLUSIONS: The combination of RT and Nivolumab had a manageable safety profile and demonstrated clinically significant disease control outside the RT fields. Periodic abscopal response data and multi-omics data are also presented. (UMIN: UMIN000026046; ClinicalTrials.gov: NCT03430479).

Citation Format: Masahiro Takada, Michio Yoshimura, Kosuke Kawaguchi, Takeshi Kotake, Ryuji Uozumi, Masako Kataoka, Takahiko Koyama, Hironori Kato, Hiroshi Yoshibayashi, Hirofumi Suwa, Wakako Tsuji, Hiroyasu Yamashiro, Tatsuki Kataoka, Hiroshi Ishiguro, Reitaro Tokumasu, Maya Honda, Rikiya Yamashita, Atsushi Yonezawa, Yuki Himoto, Natsuko Onishi, Laxmi Parida, Satoshi Morita, Masakazu Toi. A multicenter phase Ib/II study for evaluating safety and efficacy of Nivolumab in combination with radiation therapy in patients with HER2-negative metastatic breast cancer (KBCRN-B-002 trial) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-09-07.

Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation

PURPOSE

To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis.

METHODS

This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II-III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV.

RESULTS

Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%).

CONCLUSIONS

Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

OBJECTIVES

This study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans.

METHODS

In this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs.

RESULTS

Among pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively.

CONCLUSIONS

Variations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

KEY POINTS

• For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC < 0.9) in a clinical heterogeneous dataset. • Variations between CECT scans affected the number of reproducible radiomic features to a greater extent than variations in radiologist segmentation. • A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

Unraveling ultrafast dynamics of the photoswitchable bridged dithienylethenes under structural constraints

The excited state dynamics of constrained photochromic benzodithienylethenes were addressed by considering the bridging with polyether chains (from x = 4 to 6 units) at the ortho and meta positions of the aryl group, named DTE-o_x and DTE-m_x, via time-resolved absorption spectroscopy supported with (TD)-DFT calculations. The photochromic parameters and geometrical structures of these series are discussed. A novel photocyclization pathway via a triplet state, evidenced recently (Hamdi et al., Phys. Chem. Chem. Phys., 2016, 18, 28091-28100), is largely dependent on the length and the position of the polyether chain. For the first time, by comparing the two series, we revealed, for the DTE-o_x series, an interconversion not only in the ground state but also between the triplet states of the anti-parallel and parallel open form conformers.

Sorafenib for Advanced and Refractory Desmoid Tumors

BACKGROUND

Desmoid tumors (also referred to as aggressive fibromatosis) are connective tissue neoplasms that can arise in any anatomical location and infiltrate the mesentery, neurovascular structures, and visceral organs. There is no standard of care.

METHODS

In this double-blind, phase 3 trial, we randomly assigned 87 patients with progressive, symptomatic, or recurrent desmoid tumors to receive either sorafenib (400-mg tablet once daily) or matching placebo. Crossover to the sorafenib group was permitted for patients in the placebo group who had disease progression. The primary end point was investigator-assessed progression-free survival; rates of objective response and adverse events were also evaluated.

RESULTS

With a median follow-up of 27.2 months, the 2-year progression-free survival rate was 81% (95% confidence interval [CI], 69 to 96) in the sorafenib group and 36% (95% CI, 22 to 57) in the placebo group (hazard ratio for progression or death, 0.13; 95% CI, 0.05 to 0.31; P<0.001). Before crossover, the objective response rate was 33% (95% CI, 20 to 48) in the sorafenib group and 20% (95% CI, 8 to 38) in the placebo group. The median time to an objective response among patients who had a response was 9.6 months (interquartile range, 6.6 to 16.7) in the sorafenib group and 13.3 months (interquartile range, 11.2 to 31.1) in the placebo group. The objective responses are ongoing. Among patients who received sorafenib, the most frequently reported adverse events were grade 1 or 2 events of rash (73%), fatigue (67%), hypertension (55%), and diarrhea (51%).

CONCLUSIONS

Among patients with progressive, refractory, or symptomatic desmoid tumors, sorafenib significantly prolonged progression-free survival and induced durable responses. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT02066181 .).

Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging

PURPOSE

To evaluate the short-term reproducibility of radiomic features in liver parenchyma and liver cancers in patients who underwent consecutive contrast-enhanced CT (CECT) with intravenous iodinated contrast within 2 weeks by chance.

METHODS

The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients' informed consent. Patients were included if they had a liver malignancy (liver metastasis, n = 22, intrahepatic cholangiocarcinoma, n = 10, and hepatocellular carcinoma, n = 6), had two consecutive CECT within 14 days, and had no prior or intervening therapy. Liver tumors and liver parenchyma were segmented and radiomic features (n = 254) were extracted. The number of reproducible features (with concordance correlation coefficients > 0.9) was calculated for patient subgroups with different variations in contrast injection rate and pixel resolution.

RESULTS

The number of reproducible radiomic features decreased with increasing variations in contrast injection rate and pixel resolution. When including all CECTs with injection rates differences of less than 15% vs. up to 50%, 63/254 vs. 0/254 features were reproducible for liver parenchyma and 68/254 vs. 50/254 features were reproducible for malignancies. When including all CT with pixel resolution differences of 0-5% or 0-15%, 20/254 vs. 0/254 features were reproducible for liver parenchyma; 34/254 liver malignancy features were reproducible with pixel differences up to 15%.

CONCLUSION

A greater number of liver malignancy radiomic features were reproducible compared to liver parenchyma features, but the proportion of reproducible features decreased with increasing variations in contrast injection rates and pixel resolution.

18F-FDG PET/CT for Monitoring of Ipilimumab Therapy in Patients with Metastatic Melanoma

Immune checkpoint inhibitors (ICIs) are now commonly used to treat patients with metastatic malignant melanoma. Although concerns have been raised that the inflammatory response induced by ICIs may limit the ability of ¹⁸F-FDG PET/CT to assess tumor response, systematic analyses on the use of ¹⁸F-FDG PET/CT in this setting are mostly lacking. Thus, we set out to evaluate the association between tumor response on ¹⁸F-FDG PET/CT and prognosis in patients with metastatic malignant melanoma treated with ipilimumab. Methods: We analyzed 60 consecutive patients with metastatic melanoma who underwent ¹⁸F-FDG PET/CT scans both before and after treatment to evaluate treatment response after completion of ipilimumab therapy. Tumor response was assessed by the change in the sum of SULpeak (voxels with the highest average SUL [SUV normalized to lean body mass]) of up to 5 lesions according to PERCIST5. New lesions on PET that appeared suggestive of metastases were considered progressive metabolic disease (PMD). Because immunotherapy may cause new inflammatory lesions that are detectable on ¹⁸F-FDG PET/CT, we also evaluated an immunotherapy-modified response classification (imPERCIST5). In this classification, new lesions do not define PMD per se; rather, PMD requires an increase in the sum of SULpeak by 30%. The correlation between tumor response according to these 3 definitions and overall survival (OS) was evaluated and compared with known prognostic factors. Results: In responders and nonresponders, the 2-y OS was 66% versus 29% for imPERCIST5 (P = 0.003). After multivariate analysis, imPERCIST5 remained prognostic (hazard ratio, 3.853; 95% confidence interval, 1.498-9.911; P = 0.005). New sites of focal ¹⁸F-FDG uptake occurred more often in patients with PMD (n = 24) by imPERCIST5 than in those with stable metabolic disease (n = 7) or partial metabolic response (n = 4). In patients with partial metabolic response, 2 of 4 isolated new lesions regressed spontaneously during follow-up. Conclusion: In patients with metastatic melanoma treated with ipilimumab, tumor response according to PERCIST was associated with OS. Our data suggest that PMD should not be defined by the appearance of new lesions, but rather by an increase in the sum of SULpeak.

Convolutional neural networks: an overview and application in radiology

Abstract

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care.

Key Points

• Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology.

• Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm.

• Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

Evaluation of the suitability of a plant virus, pepper mild mottle virus, as a surrogate of human enteric viruses for assessment of the efficacy of coagulation-rapid sand filtration to remove those viruses

Here, we evaluated the removal of three representative human enteric viruses - adenovirus (AdV) type 40, coxsackievirus (CV) B5, and hepatitis A virus (HAV) IB - and one surrogate of human caliciviruses - murine norovirus (MNV) type 1 - by coagulation-rapid sand filtration, using water samples from eight water sources for drinking water treatment plants in Japan. The removal ratios of a plant virus (pepper mild mottle virus; PMMoV) and two bacteriophages (MS2 and φX174) were compared with the removal ratios of human enteric viruses to assess the suitability of PMMoV, MS2, and φX174 as surrogates for human enteric viruses. The removal ratios of AdV, CV, HAV, and MNV, evaluated via the real-time polymerase chain reaction (PCR) method, were 0.8-2.5-log₁₀ when commercially available polyaluminum chloride (PACl, basicity 1.5) and virgin silica sand were used as the coagulant and filter medium, respectively. The type of coagulant affected the virus removal efficiency, but the age of silica sand used in the rapid sand filtration did not. Coagulation-rapid sand filtration with non-sulfated, high-basicity PACls (basicity 2.1 or 2.5) removed viruses more efficiently than the other aluminum-based coagulants. The removal ratios of MS2 were sometimes higher than those of the three human enteric viruses and MNV, whereas the removal ratios of φX174 tended to be smaller than those of the three human enteric viruses and MNV. In contrast, the removal ratios of PMMoV were similar to and strongly correlated with those of the three human enteric viruses and MNV. Thus, PMMoV appears to be a suitable surrogate for human enteric viruses for the assessment of the efficacy of coagulation-rapid sand filtration to remove viruses.

Left Gastric Vein Visualization with Hepatopetal Flow Information in Healthy Subjects Using Non-Contrast-Enhanced Magnetic Resonance Angiography with Balanced Steady-State Free-Precession Sequence and Time-Spatial Labeling Inversion Pulse

Objective

To selectively visualize the left gastric vein (LGV) with hepatopetal flow information by non-contrast-enhanced magnetic resonance angiography under a hypothesis that change in the LGV flow direction can predict the development of esophageal varices; and to optimize the acquisition protocol in healthy subjects.

Materials and Methods

Respiratory-gated three-dimensional balanced steady-state free-precession scans were conducted on 31 healthy subjects using two methods (A and B) for visualizing the LGV with hepatopetal flow. In method A, two time-spatial labeling inversion pulses (Time-SLIP) were placed on the whole abdomen and the area from the gastric fornix to the upper body, excluding the LGV area. In method B, nonselective inversion recovery pulse was used and one Time-SLIP was placed on the esophagogastric junction. The detectability and consistency of LGV were evaluated using the two methods and ultrasonography (US).

Results

Left gastric veins by method A, B, and US were detected in 30 (97%), 24 (77%), and 23 (74%) subjects, respectively. LGV flow by US was hepatopetal in 22 subjects and stagnant in one subject. All hepatopetal LGVs by US coincided with the visualized vessels in both methods. One subject with non-visualized LGV in method A showed stagnant LGV by US.

Conclusion

Hepatopetal LGV could be selectively visualized by method A in healthy subjects.

Non-contrast-enhanced 3D MR portography within a breath-hold using compressed sensing acceleration: A prospective noninferiority study

PURPOSE

To evaluate images of non-contrast-enhanced 3D MR portography within a breath-hold (BH) using compressed sensing (CS) compared to standard respiratory-triggered (RT) sequences.

MATERIALS AND METHODS

Fifty-nine healthy volunteers underwent MR portography using two sequences of balanced steady-state free-precession (bSSFP) with time-spatial labeling inversion pulses (Time-SLIP): BH bSSFP-CS and RT bSSFP. Two radiologists independently scored the diagnostic acceptability to delineate the portal branches (MPV: main portal vein; RPV: right portal vein; LPV: left portal vein; RPPV: right posterior portal vein; and P4 and P8: portal branch of segment 4 and segment 8, respectively) and the overall image quality on a four-point scale. We assessed noninferiority of BH bSSFP-CS to RT bSSFP. For quantitative analysis, vessel-to-liver contrast (C_v-l) was calculated in MPV, RPV and LPV.

RESULTS

BH bSSFP sequence was successfully performed with a 30-second acquisition time. The diagnostic acceptability scores of BH bSSFP-CS compared with RT bSSFP were statistically noninferior: MPV (95% CI for score difference of Reader 1 and Reader 2, respectively: [-0.16, 0.06], [-0.05, 0.02]), RPV ([-0.00, 0.11], [-0.01, 0.08]), LPV ([-0.03, 0.10], [-0.10, 0.03]), RPPV ([-0.03, 0.10], [-0.05, 0.05]), P4 ([-0.13, 0.34], [-0.28, 0.21]) and P8 ([-0.21, 0.11], [-0.25, -0.02]). However, the overall image quality of BH bSSFP-CS did not show noninferiority ([-0.61, -0.24], [-0.54, -0.17]). C_v-l values were significantly lower in BH bSSFP-CS (P<0.001).

CONCLUSION

CS enabled non-contrast-enhanced 3D bSSFP MR portography to be performed within a BH while maintaining noninferior diagnostic acceptability compared to standard RT bSSFP MR portography.

Portal vein branching order helps in the recognition of anomalous right-sided round ligament: common features and variations in portal vein anatomy

PURPOSE

This study aimed to evaluate the common features and variations of portal vein anatomy in right-sided round ligament (RSRL), which can help propose a method to detect and diagnose this anomaly.

METHODS

In this retrospective study of 14 patients with RSRL, the branching order of the portal tree was analyzed, with special focus on the relationship between the dorsal branch of the right anterior segmental portal vein (P_A-D) and the lateral segmental portal vein (P_LL), to determine the common features. The configuration of the portal vein from the main portal trunk to the right umbilical portion (RUP), the inclination of the RUP, and the number and thickness of the ramifications branching from the right anterior segmental portal vein (P_A) were evaluated for variations.

RESULTS

In all subjects, the diverging point of the P_A-D was constantly distal to that of the P_LL. The portal vein configuration was I- and Z-shaped in nine and five subjects, respectively. The RUP was tilted to the right in all subjects. In Z-shaped subjects, the portal trunk between the branching point of the right posterior segmental portal vein and that of the P_LL was tilted to the left in one subject and was almost parallel to the vertical plane in four subjects. Multiple ramifications were radially distributed from the P_A in eight subjects, whereas one predominant P_A-D branched from the P_A in six subjects.

CONCLUSIONS

Based on the diverging points of the P_A-D and P_LL, we proposed a three-step method for the detection and diagnosis of RSRL.

Usefulness of breath-hold inversion recovery-prepared T1-weighted two-dimensional gradient echo sequence for detection of hepatocellular carcinoma in Gd-EOB-DTPA-enhanced MR imaging

The aim is to evaluate the diagnostic performance and the added value of breath-hold inversion recovery-prepared T1-weighted two-dimensional gradient echo (IR-2D-GRE) sequence for detection of hepatocellular carcinoma (HCC) in patients with insufficient liver parenchymal enhancement during the hepatobiliary phase (HBP) of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI). Seventeen patients with a quantitative liver-to-spleen contrast ratio of ≤1.5 on HBP images and 36 HCCs were included. Liver-to-lesion contrast ratios on HBP images obtained with IR-2D-GRE sequence were significantly higher than those with three-dimensional gradient echo sequence. The addition of IR-2D-GRE sequence during HBP of Gd-EOB-DTPA-enhanced MRI yielded higher diagnostic accuracy and improved sensitivity.

Imaging of metastases from breast cancer to uncommon sites: a pictorial review

There are three types of breast cancer recurrence which can occur after initial treatment: local, regional, and distant. Distant metastases are more frequent than local and regional recurrences. It usually occurs several years after the primary breast cancer, although it is sometimes diagnosed at the same time as the primary breast cancer. Although the common distant metastases are bone, lung and liver, breast cancer has the potential to metastasize to almost any region of the body. Early detection and treatment of distant metastases improves the prognosis, therefore radiologists and clinicians should recognize the possibility of metastasis from breast cancer and grasp the imaging characteristics. In this report, we demonstrate the imaging characteristics of metastases from breast cancer to uncommon sites.

Intraocular Ossification in the GSP/pe Chicken With Imperfect Albinism

The eyes of 2 male and 2 female GSP/pe chickens, the imperfect albino strain, were investigated at 52 weeks of age. Aged chickens of the GSP/pe colony became blind with bilateral ocular enlargement and opaque lenses. Affected eyes (bilateral in 2 males and unilateral in 2 females) were hard and difficult to section; histologic specimens were processed after decalcification. A large portion of the posterior chamber was occupied by cancellous bone containing fibrous and cartilaginous foci. Osseous tissues developed adjacent to the choroid, and no retinal pigment epithelium (RPE) was detected between osseous tissues and the choroid. Small segments of degenerate neuronal retina were scattered in the osseous tissue. The irises and ciliary bodies were deformed by osseous tissue, and the lenses had severe cataracts. These observations suggest that the intraocular osseous tissue may be derived from RPE in the hereditary incomplete-albino strain of chickens.

Non-contrast-enhanced MR portography and hepatic venography with time-spatial labeling inversion pulses: comparison of imaging with the short tau inversion recovery method and the chemical shift selective method

PURPOSE

To compare and evaluate images of non-contrast enhanced magnetic resonance (MR) portography and hepatic venography acquired with two different fat suppression methods, the chemical shift selective (CHESS) method and short tau inversion recovery (STIR) method.

MATERIALS AND METHODS

Twenty-two healthy volunteers were examined using respiratory-triggered three-dimensional true steady-state free-precession with two time-spatial labeling inversion pulses. The CHESS or STIR methods were used for fat suppression. The relative signal-to-noise ratio and contrast-to-noise ratio (CNR) were quantified, and the quality of visualization was scored.

RESULTS

Image acquisition was successfully conducted in all volunteers. The STIR method significantly improved the CNRs of MR portography and hepatic venography. The image quality scores of main portal vein and right portal vein were higher with the STIR method, but there were no significant differences. The image quality scores of right hepatic vein, middle hepatic vein, and left hepatic vein (LHV) were all higher, and the visualization of LHV was significantly better (p<0.05).

CONCLUSION

The STIR method contributes to further suppression of the background signal and improves visualization of the portal and hepatic veins. The results support using non-contrast-enhanced MR portography and hepatic venography in clinical practice.

Comparison of monopolar and bipolar diffusion weighted imaging sequences for detection of small hepatic metastases

OBJECTIVE

To compare monopolar (MP) and bipolar (BP) diffusion weighted imaging (DWI) in detecting small liver metastases.

MATERIALS AND METHODS

Eighty-eight patients underwent 3-T MRI. The signal-to-noise ratios (SNR) of the liver parenchyma and lesions, the lesion-to-liver contrast-to-noise ratios (CNR), and the detection sensitivities were compared. The lesion distortion was scored (LDS) from 4 (no distortion) to 1 (excessive distortion), dichotomised as no-distortion and distortion, and the association between detected lesions for each reader in the MP or BP DWI group and the dichotomised lesion distortion degree was assessed.

RESULT

Forty-six hepatic metastases were confirmed. The CNR with BP images showed significantly higher values than with MP (P=0.017). The detection sensitivities of the three readers were higher in the BP sequence than in MP, and one reader detected significantly more hepatic lesions with BP images (P=0.04). LDS was significantly improved with BP sequence (P=0.002). In the no-distortion group, excluding the MP DWI assessments of one reader, detection sensitivities were significantly higher than in the distortion group (P<0.001 and P=0.002, respectively).

CONCLUSION

Reduced lesion distortion improves the detection of small liver metastases, and BP is more sensitive in detecting small liver metastases than MP DWI.