Summary The critical pressure at which the pharynx collapses (Pcrit) is an objective measurement of upper airway collapsibility, an important pathogenetic factor in obstructive sleep apnoea. This study examined the inherent variability of passive Pcrit measurement during sleep and evaluated the effects of sleep stage and body posture on Pcrit. Repeated measurements of Pcrit were assessed in 23 individuals (15 male) with diagnosed obstructive sleep apnoea throughout a single overnight sleep study. Body posture and sleep stage were unrestricted. Applied upper airway pressure was repetitively reduced to obtain multiple measurements of Pcrit. In 20 subjects multiple measurements of Pcrit were obtained. The overall coefficient of repeatability for Pcrit measurement was 4.1 cm H2O. Considering only the lateral posture, the coefficient was 4.8 cm H2O. It was 3.3 cm H2O in the supine posture. Pcrit decreased from the supine to lateral posture [supine mean 2.5 cm H2O, 95% confidence interval (CI) 1.4–3.6; lateral mean 0.3 cm H2O, 95% CI −0.8–1.4, P = 0.007] but did not vary with sleep stage (P = 0.91). This study has shown that the overall coefficient of repeatability was 4.1 cm H2O, implying that the minimum detectable difference, with 95% probability, between two repeated Pcrit measurements in an individual is 4.1 cm H2O. Such variability in overnight measures of Pcrit indicates that a single unqualified value of Pcrit cannot be used to characterize an individual’s overall collapsibility during sleep. When within‐subject variability is accounted for, change in body posture from supine to lateral significantly decreases passive pharyngeal collapsibility.
Bortezomib-based induction is often used in transplant-eligible patients with myeloma. The optimal peripheral blood stem cell (PBSC) mobilisation strategy in this context is unclear. We reviewed the efficacy of G-CSF alone (G-alone) vs. G-CSF and cyclophosphamide (G-cyclo: standard dose: 1.5-2 g/m; high dose: 3-4 g/m) PBSC mobilisation strategies in 288 patients who only received bortezomib, cyclophosphamide and dexamethasone (VCD) induction prior to autograft across six apheresis centres from November 2012 to June 2017. 'Uncomplicated successful mobilisation' was defined as achieving a PBSC yield of ≥4 × 10/kg within two aphereses, without plerixafor or mobilisation-associated toxicity (predominantly febrile neutropenia, FN). Success rates were 84% in G-cyclo standard dose (6% FN), 64% in G-cyclo high dose (18% FN) and 69% in G-alone (plerixafor successfully salvaged 8/9 patients). Median total stem cell yield was significantly higher with G-cyclo, but not different between the two cyclophosphamide doses. Age greater than the median of 61 years was associated with higher failure rates (22 vs. 11%, p = 0.01) and lower PBSC yield, especially in the G-alone group. Prior radiotherapy exposure did not impact on collection success. Our observations suggest that both G-cyclo standard dose and G-alone are reasonable mobilisation strategies. The former may be preferred if salvage plerixafor is unavailable.
Our case report pertains to a 32-year-old woman initially presenting with left flank pain and gross haematuria throughout her urinary stream. CT of her kidney/ureter/bladder (CT KUB) revealed ureteric dilatation to the level of the bladder without evidence of renal calculus and subsequently a stent was inserted. She represented a month later with contralateral flank pain, and a transuretheral resection of bladder tumour was performed. Histopathological diagnosis was epithelioid angiosarcoma. Further imaging (MRI pelvis) revealed that the tumour arose from the posterior bladder wall with local invasion and regional lymph node metastasis. Ifosfamide and epirubicin chemotherapy with single-fraction radiotherapy induced significant reduction in tumour bulk, although this initial response was followed by the development of symptoms suggestive of disease progression. She died 19 months after initial diagnosis with persistent pulmonary and vertebral metastases although no autopsy was performed.
Purpose This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [68Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. Methods Three hundred thirty-seven [68Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLVauto) and total lesional uptake (TLUauto) were calculated from the automated segmentations, and Kaplan–Meier analysis was used to assess biomarker relationship with patient overall survival. Results At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan–Meier analysis of TLVauto and TLUauto showed they were significantly associated with patient overall survival (both p < 0.005). Conclusion The fully automated assessment of whole-body [68Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival. Trial registration This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
Purpose: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [68Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. Methods: 337 [68Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent (BCR) PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), precision, and sensitivity. Recall and precision were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, precision, and sensitivity. Whole-body biomarkers total lesional volume (TLVauto) and total lesional uptake (TLUauto) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival. Results: At the patient level, the accuracy, sensitivity, and precision were all >90%, with the best metric being the precision (97.2%). Precision and recall at the lesion level were 88.2% and 73.0%, respectively. DSC and precision measured at the voxel level performed within measured inter-observer variability (DSC; median = 50.7% vs. second observer = 32%, p = 0.012. Precision; median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLVauto and TLUauto showed they were significantly associated with patient overall survival (both p < 0.005). Conclusion: The fully automated assessment of whole-body [68Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability and potentially clinically useful prognostic biomarkers associated with patient overall survival. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
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