PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

Does Quantitative Ultrasound at the Calcaneus Predict an Osteoporosis Diagnosis in Postmenopausal Women from the Silesia Osteo Active Study?

The aims of the study were to assess the clinical conformity between quantitative ultrasound (QUS) and densitometry with use of the standard World Health Organization T-score thresholds to determine optimal diagnostic cutoff values for QUS T-scores in different age groups. Three hundred sixty-five postmenopausal Caucasian women were enrolled into the study and divided into two age groups (<65 y and ≥65 y). Skeletal status was assessed using QUS measurements at the calcaneus and bone densitometry at the spine and proximal femur (Hologic Explorer, Bedford, MA, USA). QUS measurement results expressed as the stiffness index (SI) correlated significantly with both femoral neck bone mineral density (r = 0.51, p < 0.0001) and lumbar spine bone mineral density (r = 0.52, p < 0.0001). On the basis of receiver operating characteristic curve analyses, the thresholds for correspondence between QUS T-score values and T-score –2.5SD in dual X-ray absorptiometry (DXA) were established. They ranged between –1.63SD and –1.70SD in relation to femoral neck DXA and between –1.22SD and –1.51SD in relation to lumbar spine DXA, depending on age category. In conclusion, the study described here confirmed that QUS measurements at the calcaneus may provide information comparable to DXA examinations at the femoral neck and lumbar spine in postmenopausal women.     

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

PurposeAuto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.Methods and MaterialsThree contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.ResultsMean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.ConclusionAuto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose–volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.     

Effectiveness,Patient Satisfaction,and Cost Reduction of Virtual Joint Replacement Clinic Follow-Up of Hip and Knee Arthroplasty

BackgroundTotal hip and knee arthroplasties are increasingly performed operations, and routine follow-up places huge demands on orthopedic services. This study investigates the effectiveness, patients’ satisfaction, and cost reduction of Virtual Joint Replacement Clinic (VJRC) follow-up of total hip arthroplasty and total knee arthroplasty patients in a university hospital. VJRC is especially valuable when in-person appointments are not advised or feasible such as during the COVID-19 pandemic.MethodsA total of 1749 patients who were invited for VJRC follow-up for knee or hip arthroplasty from January 2017 to December 2018 were included in this retrospective study. Patients were referred to VJRC after their 6-week postoperative review. Routine VJRC postoperative review was undertaken at 1 and 7 years and then 3-yearly thereafter. We evaluated the VJRC patient response rate, acceptability, and outcome. Patient satisfaction was measured in a subgroup of patients using a satisfaction survey. VJRC costs were calculated compared to face-to-face follow-up.ResultsThe VJRC had a 92.05% overall response rate. Only 7.22% required further in-person appointments with only 3% being reviewed by an orthopedic consultant. VJRC resulted in an estimated saving of £42,644 per year at our institution. The patients’ satisfaction survey showed that 89.29% of the patients were either satisfied or very satisfied with VJRC follow-up.ConclusionVJRC follow-up for hip and knee arthroplasty patients is an effective alternative to in-person clinic assessment which is accepted by patients, has high patient satisfaction, and can reduce the cost to both health services and patients.