RadPath: A Web-based System for Integrating and Correlating Radiology and Pathology Findings During Cancer Diagnosis

Development and Operationalization of an Automated Workflow for Correlation of Knee MRI and Arthroscopy Findings

OBJECTIVE

In this study, we sought to establish and evaluate an automated workflow to prospectively capture and correlate knee MRI findings with surgical findings in a large medical center.

METHODS

This retrospective analysis included data from patients who had undergone knee MRI followed by arthroscopic knee surgery within 6 months during a 2-year period (2019-2020). Discrete data were automatically extracted from a structured knee MRI report template implementing pick lists. Operative findings were recorded discretely by surgeons using a custom-built web-based telephone application. MRI findings were classified as true-positive, true-negative, false-positive, or false-negative for medial meniscus (MM), lateral meniscus (LM), and anterior cruciate ligament (ACL) tears, with arthroscopy used as the reference standard. An automated dashboard displaying up-to-date concordance and individual and group accuracy was enabled for each radiologist. Manual correlation between MRI and operative reports was performed on a random sample of 10% of cases for comparison with automatically derived values.

RESULTS

Data from 3,187 patients (1,669 male; mean age, 47 years) were analyzed. Automatic correlation was available for 60% of cases, with an overall MRI diagnostic accuracy of 93% (MM, 92%; LM, 89%; ACL, 98%). In cases reviewed manually, the number of cases that could be correlated with surgery was higher (84%). Concordance between automated and manual review was 99% when both were available (MM, 98%; LM, 100%; ACL, 99%).

CONCLUSION

This automated system was able to accurately and continuously assess correlation between imaging and operative findings for a large number of MRI examinations.

Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

Electronic Health Record-Integrated Tumor Board Application to Save Preparation Time and Reduce Errors

PURPOSE

Multidisciplinary oncology meetings, or tumor boards (TBs), ensure and facilitate communication between specialties regarding the management of cancer cases to improve patient care. The organization of TB and the preparation and presentation of patient cases are typically inefficient processes that require the exchange of patient information via e-mail, the hunting for data and images in the electronic health record, and the copying and pasting of patient data into desktop presentation software.

METHODS

We implemented a standards-based electronic health record-integrated application that automated several aspects of TB organization and preparation. We hypothesized that this application would increase the efficiency of TB preparation, reduce errors in patient entry, and enhance communication with the clinical team. Our experimental design used a prospective evaluation by pathologists who were timed in preparing for weekly TBs using both the new application and the conventional method. In addition, patient data entry errors associated with each method were tracked, and TB attendees completed a survey evaluating satisfaction with the new application.

RESULTS

The total time savings for TB preparation using the digital TB application over the conventional method was 5 hours and 19 minutes, representing a 45% reduction in preparation time (P < .01). Survey results showed that 91% of respondents preferred the digital method and believed that it improved the flow of the TB meeting. In addition, most believed that the digital method had an impact on subsequent patient care.

CONCLUSION

This study provides further evidence that new electronic systems have the potential to significantly improve the overall TB paradigm by optimizing and enhancing case organization, preparation, and presentation.

Concordance Assessment of Pathology Results with Imaging Findings after Image-Guided Biopsy

PURPOSE

To assess the impact of radiology review for discordance between pathology results from computed tomography (CT)-guided biopsies versus imaging findings performed before a biopsy.

MATERIALS AND METHODS

In this retrospective review, which is compliant with the Health Insurance Portability and Accountability Act and approved by the institutional review board, 926 consecutive CT-guided biopsies performed between January 2015 and December 2017 were included. In total, 453 patients were presented in radiology review meetings (prospective group), and the results were classified as concordant or discordant. Results from the remaining 473 patients not presented at the radiology review meetings were retrospectively classified. Times to reintervention and to definitive diagnosis were obtained for discordant cases; of these, 49 (11%) of the 453 patients were in the prospective group and 55 (12%) of the 473 patients in the retrospective group.

RESULTS

Pathology results from CT-guided biopsies were discordant with imaging in 11% (104/926) of the cases, with 57% (59/104) of these cases proving to be malignant. In discordant cases, reintervention with biopsy and surgery yielded a shorter time to definitive diagnosis (28 and 14 days, respectively) than an imaging follow-up (78 days) (P < .001). The median time to diagnosis was 41 days in the prospective group and 56 days in the retrospective group (P = .46). When radiologists evaluated the concordance between pathology and imaging findings and recommended a repeat biopsy for the discordant cases, more biopsies were performed (50% [11/22] vs 13% [4/31]; P = .005).

CONCLUSIONS

Eleven percent of CT-guided biopsies yielded pathology results that were discordant with imaging findings, with 57% of these proving to be malignant on further workup.

Defining current gaps in quality measures for cancer immunotherapy: consensus report from the Society for Immunotherapy of Cancer (SITC) 2019 Quality Summit

BACKGROUND

Quality measures are important because they can help improve and standardize the delivery of cancer care among healthcare providers and across tumor types. In an environment characterized by a rapidly shifting immunotherapeutic landscape and lack of associated long-term outcome data, defining quality measures for cancer immunotherapy is a high priority yet fraught with many challenges.

METHODS

Thus, the Society for Immunotherapy of Cancer convened a multistakeholder expert panel to, first, identify the current gaps in measures of quality cancer care delivery as it relates to immunotherapy and to, second, advance priority concepts surrounding quality measures that could be developed and broadly adopted by the field.

RESULTS

After reviewing the existing quality measure landscape employed for immunotherapeutic-based cancer care, the expert panel identified four relevant National Quality Strategy domains (patient safety, person and family-centered care, care coordination and communication, appropriate treatment selection) with significant gaps in immunotherapy-based quality cancer care delivery. Furthermore, these domains offer opportunities for the development of quality measures as they relate to cancer immunotherapy. These four quality measure concepts are presented in this consensus statement.

CONCLUSIONS

This work represents a first step toward defining and standardizing quality delivery of cancer immunotherapy in order to realize its optimal application and benefit for patients.

Current and future best practice in imaging, staging, and response assessment for Non-Hodgkin's lymphomas: the Specialist Integrated Haematological Malignancy Imaging Reporting (SIHMIR) paradigm shift

Non-Hodgkin's lymphoma (NHL) encompasses over 40 different haematological malignancies, including low and high-grade neoplasms, such as follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) respectively. A key clinical issue in the context of NHL is delayed and inaccurate diagnosis, which contributes adversely to patient morbidity and mortality. This article will address relevant imaging aspects, with particular reference to advancements in NHL imaging, including computed tomography (CT), integrated positron-emission tomography (PET)-CT, and magnetic resonance imaging (MRI). We provide multiparametric (anato-functional) imaging display items, including histological correlation. We will also introduce our original concept of "Specialist Integrated Haematological Malignancy Imaging Reporting" (SIHMIR), a paradigm shift in lymphoma radiology.

The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography

OBJECTIVE

To evaluate a tomosynthesis screening mammography automated outcomes feedback application's adoption and impact on performance.

METHODS

This prospective intervention study evaluated a feedback application that provided mammographers subsequent imaging and pathology results for patients that radiologists had personally recalled from screening. Deployed to 13 academic and 5 private practice attending radiologists, adoption was studied from March 29, 2018, to March 20, 2019. Radiologists indicated if reviewed feedback would influence future clinical decisions. For a subset of eight academic radiologists consistently interpreting screening mammograms during the study, performance metrics were compared pre-intervention (January 1, 2016, to September 30, 2017) and post-intervention (October 1, 2017 to June 30, 2018). Abnormal interpretation rate, positive predictive value of biopsies performed, sensitivity, specificity, and cancer detection rate were compared using Pearson's χ² test. Logistic regression models were fit, adjusting for age, race, breast density, prior comparison, breast cancer history, and radiologist.

RESULTS

The 18 radiologists reviewed 68.5% (1,398 of 2,042) of available feedback cases and indicated that 17.4% of cases (243 of 1,398) could influence future decisions. For the eight academic radiologist subset, after multivariable adjustment with comparison to pre-intervention, average abnormal interpretation rate decreased (from 7.5% to 6.7%, adjusted odds ratio [aOR] 0.86, P < .01), positive predictive value of biopsies performed increased (from 40.6% to 51.3%, aOR 1.48, P = .011), and specificity increased (from 93.0% to 93.9%, aOR 1.17, P < .01) post-intervention. There was no difference in cancer detection rate per 1,000 examinations (from 5.8 to 6.1, aOR 1.01, P = .91) or sensitivity (from 81.2% to 78.7%, aOR 0.84, P = .47).

CONCLUSIONS

Radiologists used a screening mammography automated outcomes feedback application. Its use decreased false-positive examinations, without evidence of reduced cancer detection.

Radiologic-Pathologic Correlation for Nondiagnostic CT-Guided Lung Biopsies Performed for the Evaluation of Lung Cancer

OBJECTIVE. For nondiagnostic CT-guided lung biopsies, we tested whether radiologicpathologic correlation could identify patients who may benefit from repeat biopsy. MATERIALS AND METHODS. In this retrospective study, 1525 lung biopsies were performed between July 2013 and June 2017, 243 of which were nondiagnostic. Of these 243 lung biopsies, 98 were performed to evaluate for lung malignancy; 17 were excluded because of insufficient follow-up, leaving a total of 81 cases. The Brock and Herder models were used to calculate risk; in addition, cases were independently blindly reviewed by two thoracic radiologists who assigned a score from 1 (probably benign) to 5 (probably malignant). The final diagnosis was established by pathology results or benignancy was established if the lesion resolved or remained stable for at least 2 years. RESULTS. Of the 81 nondiagnostic lung biopsies, initial pathology results included 33 cases of inflammation, 28 cases of normal lung tissue or insufficient sample, 10 cases of organizing pneumonia, and 10 cases of atypical cells. 42% (34/81) of cases were eventually determined to be malignant (negative predictive value [NPV] of 58%). Pathology results of organizing pneumonia had the lowest rate of malignancy (2/10 = 20%), and pathology results of atypical cells had the highest rate of malignancy (5/10 = 50%, p = 0.51). Within this highly selected cohort, the Brock and Herder models were not predictive of malignancy, with areas under the ROC curve (AUCs) of 0.52 and 0.52, respectively. Evaluation by thoracic radiologists yielded AUCs of 0.85 and 0.77. When radiologist-assigned scores of 1 and 2 were considered as benign, the NPV was 90% and 95%. CONCLUSION. Review of nondiagnostic lung biopsies for radiologic-pathologic concordance by thoracic radiologists can triage patients who may benefit from repeat biopsy.

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and clinical data. Accurate extraction and classification of both visual and latent image features Is an increasingly complex challenge due to the increased complexity and resolution of biomedical image data. In this paper, we present four deep learning-based image analysis methods from the Computational Precision Medicine (CPM) satellite event of the 21st International Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) conference. One method Is a segmentation method designed to segment nuclei in whole slide tissue images (WSIs) of adult diffuse glioma cases. It achieved a Dice similarity coefficient of 0.868 with the CPM challenge datasets. Three methods are classification methods developed to categorize adult diffuse glioma cases into oligodendroglioma and astrocytoma classes using radiographic and histologic image data. These methods achieved accuracy values of 0.75, 0.80, and 0.90, measured as the ratio of the number of correct classifications to the number of total cases, with the challenge datasets. The evaluations of the four methods indicate that (1) carefully constructed deep learning algorithms are able to produce high accuracy in the analysis of biomedical image data and (2) the combination of radiographic with histologic image information improves classification performance.

An Electronic Form for Reporting Results of Targeted Prostate Biopsy: Urology Integrated Diagnostic Report (Uro-IDR)

OBJECTIVE

To detail the development of an electronic report that graphically conveys all relevant information from targeted prostate biopsy.

METHODS

The Urology Integrated Diagnostic Report (Uro-IDR) is based on a published framework (RadPath) which enables the compilation of diagnostic data from urology, radiology, and pathology. Each component of the Uro-IDR is generated by the contributing clinician, is assembled in one document, and provides correlation of the 3 inputs at a glance. Upon completion, the Uro-IDR is automatically linked to the electronic medical record as an interactive file and can also be downloaded for offline sharing as a PDF.

RESULTS

At our institution, 1638 individual Uro-IDRs were generated between June 2016 and April 2019. There were 5715 views of these documents via the EMR. The average turnaround time for the creation of an individual report decreased from nearly 8 days at the time of its launch to 2 days after 6 months of use. The average time for report generation was 22 seconds for the pathologist and 69 seconds for the radiologist. An instructive video is linked to this article.

CONCLUSION

The Uro-IDR has proven to be a feasible, efficient, clinically useful form to concisely transmit key information about targeted prostate biopsy to both clinicians and patients.

Optimum web viewer application for DICOM whole slide image visualization in anatomical pathology

BACKGROUND AND OBJECTIVE

Digital scanners are being increasingly adopt-ed in anatomical pathology, but there is still a lack of a standardized whole slide image (WSI) format. This translates into the need for interoperability and knowledge representation for shareable and computable clinical information. This work describes a robust solution, called Visilab Viewer, able to interact and work with any WSI based on the DICOM standard.

METHODS

Visilab Viewer is a web platform developed and integrated alongside a proposed web architecture following the DICOM definition. To prepare the information of the pyramid structure proposed in DICOM, a specific module was defined. The same structure is used by a second module that aggregates on the cache browser the adjacent tiles or frames of the current user's viewport with the aim of achieving fast and fluid navigation over the tissue slide. This solution was tested and compared with three different web viewers, publicly available, with 10 WSIs.

RESULTS

A quantitative assessment was performed based on the average load time per frame together with the number of fully loaded frames. Kruskal-Wallis and Dunn tests were used to compare each web viewer latency results and finally to rank them. Additionally, a qualitative evaluation was done by 6 pathologists based on speed and quality for zooming, panning and usability. The proposed viewer obtained the best performance in both assessments. The entire architecture proposed was tested in the 2nd worldwide DICOM Connectathon, obtaining successful results with all participant scanner vendors.

CONCLUSIONS

The online tool allows users to navigate and obtain a correct visualization of the samples avoiding any restriction of format and localization. The two strategical modules allow to reduce time in displaying the slide and therefore, offer high fluidity and usability. The web platform manages not only the visualization with the developed web viewer but also includes the insertion, manipulation and generation of new DICOM elements. Visilab Viewer can successfully exchange DICOM data. Connectathons are the ultimate interoperability tests and are therefore required to guarantee that solutions as Visilab Viewer and its architecture can successfully exchange data following the DICOM standard. Accompanying demo video. (Link to Youtube video.).

Automated Radiology-Pathology Module Correlation Using a Novel Report Matching Algorithm by Organ System

OBJECTIVES AND RATIONALE

Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports, thus creating a feedback loop allowing for more frequent and timely radiology-pathology correlation.

METHODS

An experienced radiologist created a matching matrix of radiology and pathology reports. These matching rules were then exported to a novel comprehensive radiology-pathology module. All distinct radiology-pathology pairings at our institution from January 1, 2016 to July 1, 2016 were included (n = 8999). The appropriateness of each radiology-pathology report pairing was scored as either "correlative" or "non-correlative." Pathology reports relating to anatomy imaged in the specific imaging study were deemed correlative, whereas pathology reports describing anatomy not imaged with the particular study were denoted non-correlative.

RESULTS

Overall, there was 88.3% correlation (accuracy) of the radiology and pathology reports (n = 8999). Subset analysis demonstrated that computed tomography (CT) abdomen/pelvis, CT head/neck/face, CT chest, musculoskeletal CT (excluding spine), mammography, magnetic resonance imaging (MRI) abdomen/pelvis, MRI brain, musculoskeletal MRI (excluding spine), breast MRI, positron emission tomography (PET), breast ultrasound, and head/neck ultrasound all demonstrated greater than 91% correlation. When further stratified by imaging modality, CT, MRI, mammography, and PET demonstrated excellent correlation (greater than 96.3%). Ultrasound and non-PET nuclear medicine studies demonstrated poorer correlation (80%).

CONCLUSION

There is excellent correlation of radiology imaging reports and appropriate pathology reports when matched by organ system. Rapid, appropriate radiology-pathology report pairings provide an excellent opportunity to close feedback loop to the interpreting radiologist.

Imaging approaches to optimize molecular therapies

Imaging, including its use for innovative tissue sampling, is slowly being recognized as playing a pivotal role in drug development, clinical trial design, and more effective delivery and monitoring of molecular therapies. The challenge is that, while a considerable number of new imaging technologies and new targeted tracers have been developed for cancer imaging in recent years, the technologies are neither evenly distributed nor evenly implemented. Furthermore, many imaging innovations are not validated and are not ready for widespread use in drug development or in clinical trial designs. Inconsistent and often erroneous use of terminology related to quantitative imaging biomarkers has also played a role in slowing their development and implementation. We examine opportunities for, and challenges of, the use of imaging biomarkers to facilitate development of molecular therapies and to accelerate progress in clinical trial design. In the future, in vivo molecular imaging, image-guided tissue sampling for mutational analyses ("high-content biopsies"), and noninvasive in vitro tests ("liquid biopsies") will likely be used in various combinations to provide the best possible monitoring and individualized treatment plans for cancer patients.

Sensor, Signal, and Imaging Informatics

Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

Post-fine-needle aspiration biopsy communication and the integrated and standardized cytopathology report

Communication between cytopathologists and patients and their care team is a critical component of accurate and timely patient management. The most important single means of communication for the cytopathologist is through the cytopathology report. Implementation of standardized terminology schemes and structured, templated reporting facilitates the ability of the cytopathologist to provide a comprehensive and integrated report. Cytopathology has been among the pathology subspecialties that have led the way in developing standardized reporting, beginning with the 1954 Papanicolaou classification scheme for cervical-vaginal cytology and continuing through the Bethesda systems for gynecological cytology and several nongynecological cytology systems. The effective reporting of cytopathology necessarily becomes more complex as it addresses increasingly sophisticated management options, requiring the integration of information from a broader range of sources. In addition to the complexity of information inputs, a wider spectrum of consumers of these reports is emerging, from patients themselves to primary care providers to subspecialized disease management experts. Both these factors require that the reporting cytopathologist provide the integration and interpretation necessary to translate diverse forms of information into meaningful and actionable reports that will inform the care team while enabling the patient to meaningfully participate in his or her own care. To achieve such broad and focused communications will require first the development of standardized and integrated reports and ultimately the involvement of cytopathologists in the development of the clinical informatics needed to treat all these items of information as structured data elements with flexible reporting operators to address the full range of patient and patient care needs. Cancer Cytopathol 2017;125(6 suppl):486-93. © 2017 American Cancer Society.

Texture analysis of the developing human brain using customization of a knowledge-based system

Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy.Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs.Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.