SEPTEMBER 2020
PREDICT ESR Andrei Iantzen ranked 1st place in the MICCAI challenge on PET/CT head and neck tumor automated segmentation. This was part of the HECKTOR2020challenge and he was awarded the 500 euros prize sponsored by Siemens Healthineers Switzerland. 

Andrei also reached top 10 (amongst 120+ participants) in the final testing set of the BRATS 2020 MICCAI challenge using the same methodological solution as for the HECKTOR challenge

JULY 2020
Segmenting Hepatocellular Carcinoma in Multi-phase CT

Liver cancer diagnosis and treatment response assessment typically rely on the inspection of multi-phase contrast-enhanced computed tomography (CT) or magnetic resonance (MR) images. To date, various methods were proposed to automatically segment liver lesions in single time-step CT; but limited research addressed image analysis of multiple contrast phases. In this paper, we propose a multi-encoder 3D U-Net which, inspired by radiological practice, combines complementary tumour characteristics from both the arterial phase (AP) and portal venous phase (PVP) CT images. ESR Nora Vogt and colleagues demonstrate that encoder-decoder networks with disentangled feature extraction in two encoder streams outperform the baseline U-Nets that process single-phase data alone or apply input-level fusion for stacks of multi-phase data as channel input. Finally, they make use of a public single-phase CT liver tumour dataset for the pre-training of network parameters to improve the generalisation capabilities of their networks.

This conference paper was published on July 8 2020 in Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science

https://link.springer.com/chapter/10.1007/978-3-030-52791-4_7

JUNE 2020
Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies

A group of researchers among with ESR Ronrick Da-ano found that multicenter studies are needed to demonstrate the clinical potential value of radiomics as a prognostic tool. However, variability in scanner models, acquisition protocols and reconstruction settings are unavoidable and radiomic features are notoriously sensitive to these factors, which hinders pooling them in a statistical analysis. A statistical harmonization method called ComBat was developed to deal with the “batch effect” in gene expression microarray data and was used in radiomics studies to deal with the “center-effect”. Their goal was to evaluate modifications in ComBat allowing for more flexibility in choosing a reference and improving robustness of the estimation. Two modified ComBat versions were evaluated: M-ComBat allows to transform all features distributions to a chosen reference, instead of the overall mean, providing more flexibility. B-ComBat adds bootstrap and Monte Carlo for improved robustness in the estimation. BM-ComBat combines both modifications. The four versions were compared regarding their ability to harmonize features in a multicenter context in two different clinical datasets. The first contains 119 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging and positron emission tomography imaging. In that case ComBat was applied with 3 labels corresponding to each center. The second one contains 98 locally advanced laryngeal cancer patients from 5 centers with contrast-enhanced computed tomography. In that specific case, because imaging settings were highly heterogeneous even within each of the five centers, unsupervised clustering was used to determine two labels for applying ComBat. The impact of each harmonization was evaluated through three different machine learning pipelines for the modelling step in predicting the clinical outcomes, across two performance metrics (balanced accuracy and Matthews correlation coefficient). Before harmonization, almost all radiomic features had significantly different distributions between labels. These differences were successfully removed with all ComBat versions. The predictive ability of the radiomic models was always improved with harmonization and the improved ComBat provided the best results. This was observed consistently in both datasets, through all machine learning pipelines and performance metrics. The proposed modifications allow for more flexibility and robustness in the estimation. They also slightly but consistently improve the predictive power of resulting radiomic models.

This article was published on June 24 2020 in SCIENTIFIC REPORTS

https://www.nature.com/articles/s41598-020-66110-w

MARCH 2020
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns. Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives. One can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes. Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. PREDICT ESR Fadila Zerka and colleagues reviewed the major implementations of distributed learning in health care.

This review was published on March 5 in JCO Clinical Cancer Informatics.

https://ascopubs.org/doi/full/10.1200/CCI.19.00047

FEBRUARY 2020
Radiomics: from qualitative to quantitative imaging
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, PREDICT ESR's William Rogers, Sithin Thulasi, Sergey Primakov, Manon Beuque, Fadila Zerka together with colleagues describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

This review was published February 26 2020 in The British Journal of Radiology.

https://www.birpublications.org/doi/full/10.1259/bjr.20190948?
url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed

JANUARY 2020
Deep learning in fracture detection: a narrative review

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, PREDICT ESR Sergey Primakov together with colleagues provides a brief overview of deep learning technology: (1) they describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.

An article describing this review was published on January 13 in Acta Orthopaedica.

The article: https://www.tandfonline.com/doi/full/
10.1080/17453674.2019.1711323