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Dermatology Dermatology

ALK Inhibitors Do Not Increase Sensitivity to Radiation in EML4-ALK Non-small Cell Lung Cancer.

In Anticancer research

BACKGROUND/AIM : ALK inhibitors like Crizotinib, Ceritinib and Alectinib are targeted therapies used in patients with anaplastic lymphoma kinase (ALK)-positive, advanced non-small cell lung cancer (NSCLC). Since in this tumor entity radiotherapy is employed sequentially or concomitantly, potential synergistic effects were investigated, which may support the hypothesis of induced radiosensitization by using ALK inhibitors.

MATERIALS AND METHODS : Two cell lines expressing wild-type (WT) or echinoderm microtubule-associated protein-like 4 (EML4)-ALK were treated with ALK inhibitors, followed by irradiation. Cell survival, cell death, cell cycle and phosphorylation of H2A histone family, member X (H2AX) were examined.

RESULTS : Combined treatment with ALK-inhibitors plus 10 Gy-irradiation led to effects similar to those of sole radiotherapy, but was more effective than sole drug treatment.

CONCLUSION : There is no clear evidence of sensitization to radiation by treating EML4-ALK mutated cells with ALK inhibitors.

Fleschutz Kathrin, Walter Lisa, Leistner Rumo, Heinzerling Lucie


ALK inhibitors, Alectinib, Ceritinib, Crizotinib, NSCLC, anaplastic lymphoma kinase, radiation

General General

New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic?

In Journal of the neurological sciences

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressive neurodegenerative disease leading to progressive physical immobility with usually normal or mild cognitive and/or behavioural involvement. Many patients are relatively young, instructed, sensitive to new technologies, and professionally active when developing the first symptoms. Older patients usually require more time, encouragement, reinforcement and a closer support but, nevertheless, selecting user-friendly devices, provided earlier in the course of the disease, and engaging motivated carers may overcome many technological barriers. ALS may be considered a model for neurodegenerative diseases to further develop and test new technologies. From multidisciplinary teleconsults to telemonitoring of the respiratory function, telemedicine has the potentiality to embrace other fields, including nutrition, physical mobility, and the interaction with the environment. Brain-computer interfaces and eye tracking expanded the field of augmentative and alternative communication in ALS but their potentialities go beyond communication, to cognition and robotics. Virtual reality and different forms of artificial intelligence present further interesting possibilities that deserve to be investigated. COVID-19 pandemic is an unprecedented opportunity to speed up the development and implementation of new technologies in clinical practice, improving the daily living of both ALS patients and carers. The present work reviews the current technologies for ALS patients already in place or being under evaluation with published publications, prompted by the COVID-19 pandemic.

Pinto Susana, Quintarelli Stefano, Silani Vincenzo


Amyotrophic lateral sclerosis, Artificial intelligence, Brain-computer interfaces, COVID-19, Eye-tracking, Robotics, Telemedicine, Virtual reality

Surgery Surgery

Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study.

In International journal of surgery (London, England)

BACKGROUND : Deep learning has been validated as a promising technique for automatic segmentation and rapid three-dimensional (3D) reconstruction of lumbosacral structures on CT. Simulated foraminoplasty of percutaneous endoscopic transforaminal discectomy (PETD) through the Kambin triangle may benefit viability assessment of PETD at L5/S1 level.

MATERIAL AND METHODS : Medical records and radiographic data of patients with L5/S1 lumbar disc herniation (LDH) who received a single-level PETD from March 2013 to February 2018 were retrospectively collected and analyzed. Deep learning was adopted to achieve semantic segmentation of lumbosacral structures (nerve, bone, disc) on CT, and the segmented masks on reconstructed 3D models. Two observers measured the area of the Kambin triangle on 6 selected deep learning-derived 3D (DL-3D) models and ground truth-derived 3D (GT-3D) models, and intraclass correlation coefficient (ICC) was calculated to assess the test-retest and interobserver reliability. Foraminoplasty of PETD was simulated on L5/S1 lumbosacral 3D models. Patients with extended foraminoplasty or stuck canula occurs on simulations were predicted as PETD-difficult cases (Group A). The remaining patients were regarded as PETD-normal cases (Group B). Clinical information and outcomes were compared between the two groups.

RESULTS : Deep learning-derived 3D models of lumbosacral structures (nerves, bones, and disc) from thin-layer CT were reliable. The area of the Kambin triangle was 161.27 ± 40.10 mm2 on DL-3D models and 153.57 ± 32.37 mm2 on GT-3D models (p = 0.206). Reliability test revealed strong test-retest reliability (ICC between 0.947 and 0.971) and interobserver reliability of multiple measurements (ICC between 0.866 and 0.961). The average operation time was 99.62 ± 17.39 min in Group A and 88.93 ± 21.87 min in Group B (P = 0.025). No significant differences in patient-reported outcomes or complications were observed between the two groups (P > 0.05).

CONCLUSION : Deep learning achieved accurate and rapid segmentations of lumbosacral structures on CT, and deep learning-based 3D reconstructions were efficacious and reliable. Foraminoplasty simulation with deep learning-based lumbosacral reconstructions may benefit surgical difficulty prediction of PETD at L5/S1 level.

Fan Guoxin, Liu Huaqing, Wang Dongdong, Feng Chaobo, Li Yufeng, Yin Bangde, Zhou Zhi, Gu Xin, Zhang Hailong, Lu Yi, He Shisheng


Deep learning, Kambin triangle, Lumbar disc herniation, Percutaneous endoscopic transforaminal discectomy (PETD), Surgical difficulty

General General

Time-range based sequential mining for survival prediction in prostate cancer.

In Journal of biomedical informatics ; h5-index 55.0

BACKGROUND AND OBJECTIVE : Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients affect their overall survival. The present study tries to analyze the sequence of treatments given to the patients, along with the time intervals between each set of treatments. The time when medication needs to be changed may provide some useful insights into the survival outcome of the patients.

MATERIALS AND METHODS : A total of 407 metastatic prostate cancer patients' data was collected and analyzed from an Indian tertiary care center. Popular sequence mining algorithms with exact order constraint have been applied to the treatment data. Appropriate time intervals were added in the resulted frequent sequences and fed to machine learning techniques along with other clinical data.

RESULTS : The study suggests that the proposed methodology of the time range based sequence mining approach gave better results than the existing methods with 84.5% accuracy and 0.89 AUC. The time intervals in the existing sequence mining algorithms can give the clinicians some useful insights into the survival analysis and in determining the best lines of treatments for a particular patient.

Kaur Ishleen, Doja M N, Ahmad Tanvir


Cancer survival, Machine learning, Medical decision making, Sequential mining, Treatment patterns

General General

A multi-scale residual network for accelerated radial MR parameter mapping.

In Magnetic resonance imaging

A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T1 mapping results are obtained on brain and abdomen datasets and in vivo T2 mapping results are obtained on brain and knee datasets. Quantitative results for the T2 mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T1 mapping and in vivo brain results for T2 mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.

Fu Zhiyang, Mandava Sagar, Keerthivasan Mahesh B, Li Zhitao, Johnson Kevin, Martin Diego R, Altbach Maria I, Bilgin Ali


Convolutional neural networks, Deep learning, Image reconstruction, Multi-contrast imaging, T(1) mapping, T(2) mapping

Ophthalmology Ophthalmology

Glaucoma home-monitoring using a tablet-based visual field test (Eyecatcher): An assessment of accuracy and adherence over six months.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To assess accuracy and adherence of visual field (VF) home-monitoring in a pilot sample of glaucoma patients.

DESIGN : Prospective longitudinal feasibility and reliability study.

METHODS : Twenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet-perimeter (Eyecatcher), and were asked to perform one VF home-assessment per eye, per month, for 6 months (12 tests total). Before and after home-monitoring, two VF assessments were performed in-clinic using Standard Automated Perimetry (SAP; 4 tests total, per eye).

RESULTS : All 20 participants could perform monthly home-monitoring, though one participant stopped after 4 months (Adherence: 98%). There was good concordance between VFs measured at home and in the clinic (r = 0.94, P < 0.001). In 21 of 236 tests (9%) Mean Deviation deviated by more than ±3 dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets' front-facing camera (Area Under the ROC Curve = 0.78). Adding home-monitoring data to 2 SAP tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5 mins (Quartiles: 3.9 - 5.2 mins). Substantial variations in ambient illumination had no observable effect on VF measurements (r = 0.07, P = 0.320).

CONCLUSIONS : Home-monitoring of VFs is viable for some patients, and may provide clinically useful data.

Jones Pete R, Campbell Peter, Callaghan Tamsin, Jones Lee, Asfaw Daniel S, Edgar David F, Crabb David P


Glaucoma, Home Monitoring, Perimetry, Psychophysics, Visual Fields