Distributed learning to protect privacy in multi-centric clinical studies

Research in medicine has to deal with the growing amount of data about patients which are made available by modern technologies. All these data might be used to support statistical studies, and for identifying causal relations. To use these data, which are spread across hospitals, efficient merging techniques as well as policies to deal with this sensitive information are strongly needed. In this paper we introduce and empirically test a distributed learning approach, to train Support Vector Machines (SVM), that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to train algorithms without sharing any patients-related information, ensuring privacy and avoids the development of merging tools. We tested this approach on a large dataset and we described results, in terms of convergence and performance; we also provide considerations about the features of an IT architecture designed to support distributed learning computations

Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer?

Background: Muscle depletion negatively impacts treatment efficacy and survival rates in cancer. Prevention and timely treatment of muscle loss require prediction of patients at risk. We aimed to investigate the potential of skeletal muscle radiomic features to predict future muscle loss. Methods: A total of 116 patients with stage IV non-small cell lung cancer included in a randomised controlled trial (NCT01171170) studying the effect of nitroglycerin added to paclitaxel-carboplatin-bevacizumab were enrolled. In this post hoc analysis, muscle cross-sectional area and radiomic features were extracted from computed tomography images obtained before initiation of chemotherapy and shortly after administration of the second cycle. For internal cross-validation, the cohort was randomly split in a training set and validation set 100 times. We used least absolute shrinkage and selection operator method to select features that were most significantly associated with muscle loss and an area under the curve (AUC) for model performance. Results: Sixty-nine patients (59%) exhibited loss of skeletal muscle. One hundred ninety-three features were used to construct a prediction model for muscle loss. The average AUC was 0.49 (95% confidence interval [CI]: 0.36, 0.62). Differences in intensity and texture radiomic features over time were seen between patients with and without muscle loss. Conclusions: The present study shows that skeletal muscle radiomics did not predict future muscle loss during chemotherapy in non-small cell lung cancer. Differences in radiomic features over time might reflect myosteatosis. Future imaging analysis combined with muscle tissue analysis in patients and in experimental models is needed to unravel the biological processes linked to the radiomic features. (C) 2019 The Authors. Published by Elsevier Ltd

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals (“privacy-preserving” distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference  0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models

Bone marrow gene transfer in three patients with adenosine deaminase deficiency

Adenosine deaminase (ADA) deficiency results in severe combined immune deficiency disease (SCID), which is fatal without treatment. Allogeneic bone marrow transplantation (BMT) is the treatment of choice if an HLA-identical sibling bone marrow donor is available, resulting in almost 100% cure rate. BMT-related mortality is high in patients lacking such a donor. For these patients, efficient transfer of a recombinant ADA gene into hematopoietic stem cells is a therapeutic option if it results in the outgrowth of a 'genetically repaired' lymphoid system. Based on successful gene transfer studies in monkeys, we performed retrovirus-mediated gene transfer into CD34+ bone marrow cells of three patients with ADA deficiency. Two patients received bovine ADA conjugated to polyethylene glycol (PEG-ADA); in the third patient, PEG-ADA was started 4 months after gene transfer. Gene transfer resulted in a 5-12% transduction frequency of in vitro colony forming cells (CFU-Cs). No toxicity was observed during and after infusion of the graft. Following infusion of the transduced CD34+ cells, transduced granulocytes and mononuclear cells persisted in the circulation for 3 months. In addition, the gene was present in the marrow of one of the patients at 6 months after gene transfer. Expression of the gene was not detected. After this period, the gene could not be detected. In monkey studies we showed that myeloablation, which was not performed in the patients, may enhance engraftment of genetically modified cells. We hypothesize that lack of myeloablation, administration of bovine ADA and low numbers of transduced progenitor cells all may have contributed to the relative low numbers of transduced cells in the patients. Under these conditions, no selective advantage of the genetically corrected progenitor cells was observed

Fatal <it>Mycobacterium colombiense</it>/cytomegalovirus coinfection associated with acquired immunodeficiency due to autoantibodies against interferon gamma: a case report

<p>Abstract</p> <p>Background</p> <p>Reports of acquired immunodeficiency due to autoantibodies against interferon gamma in the adult population are increasing. The interleukin-12-dependent interferon-gamma axis is a major regulatory pathway of cell-mediated immunity and is critical for protection against a few intracellular organisms, including non-tuberculous mycobacteria and <it>Salmonella</it> spp. We report the first case of a fatal disseminated <it>Mycobacterium colombiense</it>/cytomegalovirus coinfection in an adult woman associated with the acquisition of autoantibodies against interferon-gamma.</p> <p>Case presentation</p> <p>A 49-year-old woman, born to nonconsanguineous parents in Laos, but who had lived in Canada for the past 30 years, presented with a 1-month history of weight loss, fatigue, cough, and intermittent low-grade fever. A thoracic computed tomography scan revealed an 8 × 7 cm irregular mass impacting the right superior lobar bronchus along with multiple mediastinal and hilar adenopathies. On the fourth day of admission, the patient developed fever with purulent expectorations. Treatment for a post-obstructive bacterial pneumonia was initiated while other investigations were being pursued. Almost every culture performed during the patient’s hospitalization was positive for <it>M. colombiense</it>. Given the late presentation of symptoms - at the age of 49 years - and the absence of significant family or personal medical history, we suspected an acquired immunodeficiency due to the presence of anti-interferon-gamma autoantibodies. This was confirmed by their detection at high levels in the plasma and a STAT1 phosphorylation assay on human monocytes. The final diagnosis was immunodeficiency secondary to the production of autoantibodies against interferon-gamma, which resulted in a post-obstructive pneumonia and disseminated infection of <it>M. colombiense</it>. The clinical course was complicated by the presence of a multiresistant <it>Pseudomonas aeruginosa</it> post-endobronchial ultrasound mediastinitis, cytomegalovirus pneumonitis with dissemination, and finally, susceptible <it>P. aeruginosa</it> ventilator-associated pneumonia with septic shock and multiple organ failure, leading to death despite appropriate antibacterial and anti-mycobacterial treatment.</p> <p>Conclusions</p> <p>Although rare, acquired immunodeficiency syndromes should be considered in the differential diagnosis of patients with severe, persistent, or recurrent infections. Specifically, severe non-tuberculous mycobacteria or <it>Salmonella</it> infections in adults without any other known risk factors may warrant examination of autoantibodies against interferon-gamma because of their increasing recognition in the literature.</p

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.MethodsWe collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).ResultsRandom forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.ConclusionRandom forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection