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Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases.

ABSTRACT: The TB Portals program provides a publicly accessible repository of TB case data containing multi-modal information such as case clinical characteristics, pathogen genomics, and radiomics. The real-world resource contains over 3400 TB cases, primarily drug resistant cases, and CT images with radiologist annotations are available for many of these cases. The breadth of data collected offers a patient-centric view into the etiology of the disease including the temporal context of the available imaging information. Here, we analyze a cohort of new TB cases with available radiologist observations of CTs taken around the time of initial registration of the case into the database and with available follow up to treatment outcome of cured or died. Follow up ranged from 5 weeks to a little over 2 years consistent with the longest treatment regimens for drug resistant TB and cases were registered within the years 2008 to 2019. The radiologist observations were incorporated into machine learning pipelines to test various class balancing strategies on the performance of predictive models. The modeling results support that the radiologist observations are predictive of treatment outcome. Moreover, inferential statistical analysis identifies markers of TB disease spread as having an association with poor treatment outcome including presence of radiologist observations in both lungs, swollen lymph nodes, multiple cavities, and large cavities. While the initial results are promising, further data collection is needed to incorporate methods to mitigate potential confounding such as including additional model covariates or matching cohorts on covariates of interest (e.g. demographics, BMI, comorbidity, TB subtype, etc.). Nonetheless, the preliminary results highlight the utility of the resource for hypothesis generation and exploration of potential biomarkers of TB disease severity and support these additional data collection efforts.

SUBMITTER: Rosenfeld G

PROVIDER: S-EPMC7968673 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.MethodsPatients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.ResultsOverall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.ConclusionsAutomated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Project description:ImportanceAssessment of response after radiotherapy (RT) using 18F-fluorodeoxyglucose positron emission tomography (PET) with computed tomography (CT) is routine in managing head and neck squamous cell carcinoma (HNSCC). Freeform reporting may contribute to a clinician's misunderstanding of the nuclear medicine (NM) physician's image interpretation, with important clinical implications.ObjectiveTo assess clinician-perceived freeform report meaning and discordance with NM interpretation using the modified Deauville score (MDS).Design, setting, and participantsIn this retrospective cohort study that was conducted at an academic referral center and National Cancer Institute-designated Comprehensive Cancer Center and included patients with HNSCC treated with RT between January 2014 and December 2019 with a posttreatment PET/CT and 1 year or longer of follow-up, 4 masked clinicians independently reviewed freeform PET/CT reports and assigned perceived MDS responses. Interrater reliability was determined. Clinician consensus-perceived MDS was then compared with the criterion standard NM MDS response derived from image review. Data analysis was conducted between December 2021 and February 2022.ExposuresPatients were treated with RT in either the definitive or adjuvant setting, with or without concurrent chemotherapy. They then underwent posttreatment PET/CT response assessment.Main outcomes and measuresClinician-perceived (based on the freeform PET/CT report) and NM-defined response categories were assigned according to MDS. Clinical outcomes included locoregional control, progression-free survival, and overall survival.ResultsA total of 171 patients were included (45 women [26.3%]; median [IQR] age, 61 [54-65] years), with 149 (87%) with stage III to IV disease. Of these patients, 52 (30%) received postoperative RT and 153 (89%) received concurrent chemotherapy. Interrater reliability was moderate (κ = 0.68) among oncology clinicians and minimal (κ = 0.36) between clinician consensus and NM. Exact agreement between clinician consensus and the NM was 64%. The NM-rated MDS was significantly associated with locoregional control, progression-free survival, and overall survival.Conclusions and relevanceThe results of this cohort study suggest that considerable variation in perceived meaning exists among oncology clinicians reading freeform HNSCC post-RT PET/CT reports, with only minimal agreement between MDS derived from clinician perception and NM image interpretation. The NM use of a standardized reporting system, such as MDS, may improve clinician-NM communication and increase the value of HNSCC post-RT PET/CT reports.

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Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases.

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