Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from −0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.
The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.
MATERIALS AND METHODS:
We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset.
RESULTS:
The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (−5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017–2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017–2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data.
CONCLUSIONS:
Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.
<div>Abstract<p>Purpose: We evaluated the efficacy of bavituximab – a monoclonal antibody with anti-angiogenic and immunomodulatory properties – in newly diagnosed glioblastoma (GBM) patients who also received radiation and temozolomide. Perfusion MRI and myeloid-related gene transcription and inflammatory infiltrates in pre-and post-treatment tumor specimens were studied to evaluate on-target effects (NCT03139916). Patients and Methods: Thirty-three adults with <i>isocitrate-dehydrogenase</i>-wild-type GBM received 6 weeks of concurrent chemoradiation, followed by 6 cycles of temozolomide (C1-C6). Bavituximab was given weekly, starting week 1 of chemoradiation, for at least 18 weeks. The primary endpoint was proportion of patients alive at 12 months (OS-12). The null hypothesis would be rejected if OS-12 was ≥72%. Relative cerebral blood flow (rCBF) and vascular permeability (Ktrans) were calculated from perfusion MRIs. Peripheral blood mononuclear cells (PBMCs) and tumor tissue were analyzed pre-treatment and at disease progression using RNA transcriptomics and multispectral immunofluorescence for myeloid-derived suppressor cells (MDSCs) and macrophages. Results: The study met its primary endpoint with an OS-12 of 73% (95% CI 59-90%). Decreased pre-C1 rCBF (HR 4.63, p=0.029) and increased pre-C1 Ktrans were associated with improved OS (HR 0.09, p=0.005). Pre-treatment, overexpression of myeloid-related genes in tumor tissue was associated with longer survival. Post-treatment, tumor specimens contained fewer immunosuppressive MDSCs (p=0.01). Discussion: Bavituximab has activity in newly diagnosed GBM and resulted in on-target depletion of intratumoral immunosuppressive MDSCs. Elevated pre-treatment expression of myeloid-related transcripts in GBM may predict response to bavituximab.</p></div>
Purpose Although adjuvant chemoradiotherapy for resected gallbladder cancer may improve survival for some patients, identifying which patients will benefit remains challenging because of the rarity of this disease. The specific aim of this study was to create a decision aid to help make individualized estimates of the potential survival benefit of adjuvant chemoradiotherapy for patients with resected gallbladder cancer. Methods Patients with resected gallbladder cancer were selected from the Surveillance, Epidemiology, and End Results (SEER) –Medicare database who were diagnosed between 1995 and 2005. Covariates included age, race, sex, stage, and receipt of adjuvant chemotherapy or chemoradiotherapy (CRT). Propensity score weighting was used to balance covariates between treated and untreated groups. Several types of multivariate survival regression models were constructed and compared, including Cox proportional hazards, Weibull, exponential, log-logistic, and lognormal models. Model performance was compared using the Akaike information criterion. The primary end point was overall survival with or without adjuvant chemotherapy or CRT. Results A total of 1,137 patients met the inclusion criteria for the study. The lognormal survival model showed the best performance. A Web browser–based nomogram was built from this model to make individualized estimates of survival. The model predicts that certain subsets of patients with at least T2 or N1 disease will gain a survival benefit from adjuvant CRT, and the magnitude of benefit for an individual patient can vary. Conclusion A nomogram built from a parametric survival model from the SEER-Medicare database can be used as a decision aid to predict which gallbladder patients may benefit from adjuvant CRT.
Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)