Implementation of a parallel unstructured Euler solver on shared and distributed memory architectures

An efficient three dimensional unstructured Euler solver is parallelized on a Cray Y-MP C90 shared memory computer and on an Intel Touchstone Delta distributed memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between two differing architectures are made

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN

Эффективность капецитабина по сравнению с 5-фторурацилом при раке толстой кишки и желудка: обновленный метаанализ выживаемости в шести клинических исследованиях

Оральный фторпиримидин — капецитабин — широко изучен в сравнительных исследованиях с вводимым внутривенно 5-фторурацилом как монотерапевтическое средство или в комплексном приме- нении при метастатическом колоректальном раке (МКРР) и метастатическом раке желудка (МРЖ). По рекомендации Европейских органов здравоохранения выполнен метаанализ эффективности применения капецитабина по сравнению с 5-фторурацилом при МКРР и МРЖ

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

XELOX vs FOLFOX-4 as first-line therapy for metastatic colorectal cancer: NO16966 updated results

BACKGROUND: We report updated overall survival (OS) data from study NO16966, which compared capecitabine plus oxaliplatin (XELOX) vs 5-fluorouracil/folinic acid plus oxaliplatin (FOLFOX4) as first-line therapy in metastatic colorectal cancer. METHODS: NO16966 was a randomised, two-arm, non-inferiority, phase III comparison of XELOX vs FOLFOX4, which was subsequently amended to a 2 x 2 factorial design with further randomisation to bevacizumab or placebo. A planned follow-up exploratory analysis of OS was performed. RESULTS: The intent-to-treat (ITT) population comprised 2034 patients (two-arm portion, n = 634; 2 x 2 factorial portion, n 1400). For the whole NO16966 study population, median OS was 19.8 months in the pooled XELOX/XELOX-placebo/XELOX-bevacizumab arms vs 19.5 months in the pooled FOLFOX4/FOLFOX4-placebo/FOLFOX4-bevacizumab arms (hazard ratio 0.95 (97.5% CI 0.85-1.06)). In the pooled XELOX/XELOX-placebo arms, median OS was 19.0 vs 18.9 months in the pooled FOLFOX4/FOLFOX4-placebo arms (hazard ratio 0.95 (97.5% CI 0.83-1.09)). FOLFOX4 was associated with more grade 3/4 neutropenia/granulocytopenia and febrile neutropenia than XELOX, and XELOX with more grade 3 diarrhoea and grade 3 hand-foot syndrome than FOLFOX4. CONCLUSION: Updated survival data from study NO16966 show that XELOX is similar to FOLFOX4, confirming the primary analysis of progression-free survival. XELOX can be considered as a routine first-line treatment option for patients with metastatic colorectal cancer

Epidemiology and natural history of central venous access device use and infusion pump function in the NO16966 trial

Background: Central venous access devices in fluoropyrimidine therapy are associated with complications; however, reliable data are lacking regarding their natural history, associated complications and infusion pump performance in patients with metastatic colorectal cancer.<p></p> Methods: We assessed device placement, use during treatment, associated clinical outcomes and infusion pump perfomance in the NO16966 trial.<p></p> Results: Device replacement was more common with FOLFOX-4 (5-fluorouracil (5-FU)+oxaliplatin) than XELOX (capecitabine+oxaliplatin) (14.1% vs 5.1%). Baseline device-associated events and post-baseline removal-/placement-related events occurred more frequently with FOLFOX-4 than XELOX (11.5% vs 2.4% and 8.5% vs 2.1%). Pump malfunctions, primarily infusion accelerations in 16% of patients, occurred within 1.6–4.3% of cycles. Fluoropyrimidine-associated grade 3/4 toxicity was increased in FOLFOX-4-treated patients experiencing a malfunction compared with those who did not (97 out of 155 vs 452 out of 825 patients), predominantly with increased grade 3/4 neutropenia (53.5% vs 39.8%). Febrile neutropenia rates were comparable between patient cohorts±malfunction. Efficacy outcomes were similar in patient cohorts±malfunction.<p></p> Conclusions: Central venous access device removal or replacement was common and more frequent in patients receiving FOLFOX-4. Pump malfunctions were also common and were associated with increased rates of grade 3/4 haematological adverse events. Oral fluoropyrimidine-based regimens may be preferable to infusional 5-FU based on these findings

Association of progression-free survival with patient-reported outcomes and survival: results from a randomised phase 3 trial of panitumumab

In a randomised phase 3 trial, panitumumab significantly improved progression-free survival (PFS) in patients with refractory metastatic colorectal cancer (mCRC). This analysis characterises the association of PFS with CRC symptoms, health-related quality of life (HRQoL), and overall survival (OS). CRC symptoms (NCCN/FACT CRC symptom index, FCSI) and HRQoL (EQ-5D) were assessed for 207 panitumumab patients and 184 best supportive care (BSC) patients who had at least one post-baseline patient-reported outcome (PRO) assessment. Patients alive at week 8 were included in the PRO and OS analyses and categorised by their week 8 progression status as follows: no progressive disease (no PD; best response of at least stable disease) vs progressive disease (PD). Standard imputation methods were used to assign missing values. Significantly more patients were progression free at weeks 8–24 with panitumumab vs BSC. After excluding responders, a significant difference in PFS remained favouring panitumumab (HR=0.63, 95% CI=0.52–0.77; P<0.0001). At week 8, lack of disease progression was associated with significantly and clinically meaningful lower CRC symptomatology for both treatment groups and higher HRQoL for panitumumab patients only. Overall survival favoured no PD patients vs PD patients alive at week 8. Lack of disease progression was associated with better symptom control, HRQoL, and OS

Surgery with curative-intent in patients treated with first-line chemotherapy plus bevacizumab for metastatic colorectal cancer First BEAT and the randomised phase-III NO16966 trial

BACKGROUND: Complete resection of metastases can result in cure for selected patients with metastatic colorectal cancer. METHODS: First BEAT evaluated the safety of bevacizumab with first-line chemotherapy in 1914 patients. Prospectively collected data from 225 patients who underwent curative-intent surgery were analysed, including an exploratory comparison of resection rate in patients treated with different regimens. NO16966 compared efficacy of oxaliplatin-based chemotherapy plus bevacizumab or placebo in 1400 patients. A retrospective analysis of resection rate was undertaken. RESULTS: In First BEAT, 225 out of 1914 patients (11.8%) underwent curative-intent surgery at median 64 days ( range 42-100) after the last dose of bevacizumab. R0 resection was achieved in 173 out of 225 patients (76.9%). There were no surgery-related deaths and serious post-operative complications were uncommon, with grade 3/4 bleeding and wound-healing events reported in 0.4% and 1.8%, respectively. Resection rates were highest in patients receiving oxaliplatin-based combination chemotherapy (P=0.002), possibly confounded by patient selection. In NO16966, 44 out of 699 patients treated with bevacizumab (6.3%) and 34 out of 701 patients treated with placebo (4.9%) underwent R0 metastasectomy (P=0.24). CONCLUSIONS: The rate of serious post-operative complications in First BEAT was comparable to historical controls without bevacizumab. In NO16966, there were no statistically significant differences in resection rates or overall survival in patients treated with bevacizumab vs placebo

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

CaGrid Workflow Toolkit: A taverna based workflow tool for cancer grid

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