Scientific evidence regarding microclimate and its effects on the risk of pressure ulcers (PU) remains sparse. It is known that elevated skin temperatures and moisture may affect metabolic demand as well as the mechanical behaviour of the tissue. In this study, we incorporated these microclimate factors into a novel, 3-dimensional multi-physics coupled model of the human buttocks, which simultaneously determines the biothermal and biomechanical behaviours of the buttocks in supine lying on different support surfaces. We compared 3 simulated thermally controlled mattresses with 2 reference foam mattresses. A tissue damage score was numerically calculated in a relevant volume of the model, and the cooling effect of each 1°C decrease of tissue temperature was deduced. Damage scores of tissues were substantially lower for the non-foam mattresses compared with the foams. The percentage tissue volume at risk within the volume of interest was found to grow exponentially as the average tissue temperature increased. The resultant average sacral skin temperature was concluded to be a good predictor for an increased risk of PU/injuries. Each 1°C increase contributes approximately 14 times as much to the risk with respect to an increase of 1 mmHg of pressure. These findings highlight the advantages of using thermally controlled support surfaces as well as the need to further assess the potential damage that may be caused by uncontrolled microclimate conditions on inadequate support surfaces in at-risk patients.

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Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.

Highlights Radiomics quantitatively captures visually inappreciable imaging features. PET/CT radiomics provides wholistic metabolic and structural tumor characterization. Machine-learning algorithms can generate radiomics-based biomarkers for OPSCC. PET/CT radiomics can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. Such biomarkers may improve patient selection for treatment de-intensification trials.

BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors. PURPOSE: Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance. DATA SOURCES: Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.STUDY SELECTION: From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.DATA ANALYSIS: Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool. DATA SYNTHESIS:The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.LIMITATIONS: Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.CONCLUSIONS: Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the receiver operating characteristic curve; CNN ¼ convolutional neural network; ML ¼ machine learning; PCNSL ¼ primary CNS lymphoma; PRISMA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROBAST ¼ Prediction model study Risk Of Bias Assessment Tool; TRIPOD ¼ Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis G liomas are the most common primary malignancy of the CNS. 1 An important differential diagnosis for gliomas is primary CNS lymphoma (PCNSL), a more uncommon but highly malignant neoplasia. 2 Correct differentiation of these tumor entities is an important challenge for clinicians because

Background and purpose: Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT). Methods:We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearmanʼs correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

ObjectivesTo systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.MethodsThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9.ResultsThe search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%).ConclusionsThe application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice.Systematic Review RegistrationPROSPERO, identifier CRD42020209938.

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