Towards texture accurate slice interpolation of medical images using PixelMiner.

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Survival outcomes of patients with advanced melanoma from 2013 to 2017: Results of a nationwide population-based registry.

BACKGROUND: The treatment landscape has completely changed for advanced melanoma. We report survival outcomes and the differential impact of prognostic factors over time in daily clinical practice. METHODS: From a Dutch nationwide population-based registry, patients with advanced melanoma diagnosed from 2013 to 2017 were analysed (n = 3616). Because the proportional hazards assumption was violated, a multivariable Cox model restricted to the first 6 months and a multivariable landmark Cox model from 6 to 48 months were used to assess overall survival (OS) of cases without missing values. The 2017 cohort was excluded from this analysis because of the short follow-up time. RESULTS: Median OS of the 2013 and 2016 cohort was 11.7 months (95% confidence interval [CI]: 10.4-13.5) and 17.7 months (95% CI: 14.9-19.8), respectively. Compared with the 2013 cohort, the 2016 cohort had superior survival in the Cox model from 0 to 6 months (hazard ratio [HR] = 0.55 [95% CI: 0.43-0.72]) and in the Cox model from 6 to 48 months (HR = 0.68 [95% CI: 0.57-0.83]). Elevated lactate dehydrogenase levels, distant metastases in ≥3 organ sites, brain and liver metastasis and Eastern Cooperative Oncology Group performance score of ≥1 had stronger association with inferior survival from 0 to 6 months than from 6 to 48 months. BRAF-mutated melanoma had superior survival in the first 6 months (HR = 0.50 [95% CI: 0.42-0.59]). CONCLUSION(S): Prognosis for advanced melanoma in the Netherlands has improved from 2013 to 2016. Prognostic importance of most evaluated factors was higher in the first 6 months after diagnosis. BRAF-mutated melanoma was only associated with superior survival in the first 6 months.

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.

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.