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Machine learning- and WGCNA-mediated double analysis based on genes associated with disulfidptosis, cuproptosis and ferroptosis for the construction and validation of the prognostic model for breast cancer
CONCLUSION: Multiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients.PMID:37712959 | DOI:10.1007/s00432-023-05378-7
Source: Clinical Breast Cancer - September 15, 2023 Category: Cancer & Oncology Authors: Lijun Xu Shanshan Wang Dan Zhang Yunxi Wu Jiali Shan Huixia Zhu Chongyu Wang Qingqing Wang Source Type: research
TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance
Phys Med Biol. 2023 Sep 15. doi: 10.1088/1361-6560/acfa5e. Online ahead of print.ABSTRACTOBJECTIVE: Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range d...
Source: Physics in Medicine and Biology - September 15, 2023 Category: Physics Authors: Lingpeng Zeng Minghui Zhang Yun Zhang Zhongsheng Zou Yu Guan Bin Huang Xiuwen Yu Shenggou Ding Qiegen Liu Changfei Gong Source Type: research
Analysis and prediction of liver volume change maps derived from computational tomography scans acquired pre- and post-radiation therapy
This study leverages the use of deep learning-based segmentation and biomechanical deformable image registration (DIR) to analyze and predict this relationship. Pre- and Post-EBRT imaging data were collected for 100 patients treated for HCC, CC or CRC with IMRT with prescription doses ranging from 50 to 100 Gy delivered in 10 to 28 fractions. For each patient, DIR between the portal and venous (PV) phase of a diagnostic CT scan acquired before RT planning, and a PV phase of a diagnostic CT scan acquired after the end of RT (on average 147±36 days) was performed to calculate Jacobian maps representing volume changes in the...
Source: Physics in Medicine and Biology - September 15, 2023 Category: Physics Authors: Guillaume Cazoulat Aashish C Gupta Mais M Al Taie Eugene J Koay Kristy K Brock Source Type: research
Ring artifacts correction for computed tomography image using unsupervised contrastive learning
Phys Med Biol. 2023 Sep 15. doi: 10.1088/1361-6560/acfa60. Online ahead of print.ABSTRACTComputed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis. To address this challenge, we propose a novel method based on dual contrast learning image style transformation network model (DCLGAN) that effectively eliminates ring artifacts from CT images while preserving texture details. 
Approach: Our method involves simulating ri...
Source: Physics in Medicine and Biology - September 15, 2023 Category: Physics Authors: Tangsheng Wang Xuan Liu Chulong Zhang Yutong He Yinping Chan Yaoqin Xie Xiaokun Liang Source Type: research
Functional outcome prediction after spinal cord injury using ensemble machine learning
CONCLUSIONS: Our study revealed that functional prognostication could be achieved using machine-learning methods with features present at the time of rehabilitation admission. Goals can be set at the beginning of rehabilitation. Moreover, our model can be utilized to evaluate advanced medical treatments, such as regenerative medicine.PMID:37714506 | DOI:10.1016/j.apmr.2023.08.011
Source: Health Physics - September 15, 2023 Category: Physics Authors: Chihiro Kato Osamu Uemura Yasunori Sato Tetsuya Tsuji Source Type: research
Development of a national deep learning-based auto-segmentation model for the heart on clinical delineations from the DBCG RT nation cohort
CONCLUSIONS: This study demonstrated a deep learning-based auto-segmentation model trained on curated clinical delineations which performs on par with a model trained on dedicated delineations, making it easier to develop multi-institutional auto-segmentation models.PMID:37712509 | DOI:10.1080/0284186X.2023.2252582
Source: Acta Oncologica - September 15, 2023 Category: Cancer & Oncology Authors: Emma Riis Skars ø Lasse Refsgaard Abhilasha Saini Ditte Sloth M øller Ebbe Laugaard Lorenzen Else Maae Karen Andersen Maja Vestm ø Maraldo Marie Louise Milo Tine Bisballe Nyeng Birgitte Vrou Offersen Stine Sofia Korreman Source Type: research
Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair
CONCLUSIONS: In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.PMID:37710397 | DOI:10.1093/bjs/znad287
Source: The British Journal of Surgery - September 15, 2023 Category: Surgery Authors: Ben Li Badr Aljabri Raj Verma Derek Beaton Naomi Eisenberg Douglas S Lee Duminda N Wijeysundera Thomas L Forbes Ori D Rotstein Charles de Mestral Muhammad Mamdani Graham Roche-Nagle Mohammed Al-Omran Source Type: research
TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance
Phys Med Biol. 2023 Sep 15. doi: 10.1088/1361-6560/acfa5e. Online ahead of print.ABSTRACTOBJECTIVE: Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range d...
Source: Physics in Medicine and Biology - September 15, 2023 Category: Physics Authors: Lingpeng Zeng Minghui Zhang Yun Zhang Zhongsheng Zou Yu Guan Bin Huang Xiuwen Yu Shenggou Ding Qiegen Liu Changfei Gong Source Type: research
Analysis and prediction of liver volume change maps derived from computational tomography scans acquired pre- and post-radiation therapy
This study leverages the use of deep learning-based segmentation and biomechanical deformable image registration (DIR) to analyze and predict this relationship. Pre- and Post-EBRT imaging data were collected for 100 patients treated for HCC, CC or CRC with IMRT with prescription doses ranging from 50 to 100 Gy delivered in 10 to 28 fractions. For each patient, DIR between the portal and venous (PV) phase of a diagnostic CT scan acquired before RT planning, and a PV phase of a diagnostic CT scan acquired after the end of RT (on average 147±36 days) was performed to calculate Jacobian maps representing volume changes in the...
Source: Physics in Medicine and Biology - September 15, 2023 Category: Physics Authors: Guillaume Cazoulat Aashish C Gupta Mais M Al Taie Eugene J Koay Kristy K Brock Source Type: research
Deep learning in MRI-guided radiation therapy: A systematic review
J Appl Clin Med Phys. 2023 Sep 15:e14155. doi: 10.1002/acm2.14155. Online ahead of print.ABSTRACTRecent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods,...
Source: Journal of Applied Clinical Medical Physics - September 15, 2023 Category: Physics Authors: Zach Eidex Yifu Ding Jing Wang Elham Abouei Richard L J Qiu Tian Liu Tonghe Wang Xiaofeng Yang Source Type: research
Physical model-based cascaded generative adversarial networks for accelerating quantitative multi-parametric magnetic resonance imaging
CONCLUSION: Compared with other existing methods, the physical model-based cascaded generative adversarial networks can reconstruct more image details and features, thus improving the quality and accuracy of the reconstructed images.PMID:37712278 | PMC:PMC10505569 | DOI:10.12122/j.issn.1673-4254.2023.08.18
Source: Journal of Southern Medical University - September 15, 2023 Category: Universities & Medical Training Authors: Y Liu Z Chu Y Zhang Source Type: research
A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities
CONCLUSION: The proposed deep learning algorithm model constructed based on the image characteristics of the APT modality is effective for glioma IDH genotyping and identification task and may potentially replace the commonly used T1CE modality to avoid contrast agent injection and achieve non- invasive IDH genotyping.PMID:37712275 | PMC:PMC10505564 | DOI:10.12122/j.issn.1673-4254.2023.08.15
Source: Journal of Southern Medical University - September 15, 2023 Category: Universities & Medical Training Authors: Z Chu Y Qu T Zhong S Liang Z Wen Y Zhang Source Type: research
Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects
This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.PMID:37712476 | DOI:10.1213/ANE.0000000000006679
Source: Anesthesia and Analgesia - September 15, 2023 Category: Anesthesiology Authors: Hannah Lonsdale Geoffrey M Gray Luis M Ahumada Clyde T Matava Source Type: research
Machine learning- and WGCNA-mediated double analysis based on genes associated with disulfidptosis, cuproptosis and ferroptosis for the construction and validation of the prognostic model for breast cancer
CONCLUSION: Multiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients.PMID:37712959 | DOI:10.1007/s00432-023-05378-7
Source: Clinical Genitourinary Cancer - September 15, 2023 Category: Cancer & Oncology Authors: Lijun Xu Shanshan Wang Dan Zhang Yunxi Wu Jiali Shan Huixia Zhu Chongyu Wang Qingqing Wang Source Type: research
Raising a Bacterium to the Rank of a Model System: The < em > Listeria < /em > Paradigm
Annu Rev Microbiol. 2023 Sep 15;77:1-22. doi: 10.1146/annurev-micro-110422-112841.ABSTRACTMy scientific career has resulted from key decisions and reorientations, sometimes taken rapidly but not always, guided by discussions or collaborations with amazing individuals from whom I learnt a lot scientifically and humanly. I had never anticipated that I would accomplish so much in what appeared as terra incognita when I started to interrogate the mechanisms underlying the virulence of the bacterium Listeria monocytogenes. All this has been possible thanks to a number of talented team members who ultimately became friends.PMID:...
Source: Annual Review of Microbiology - September 15, 2023 Category: Microbiology Authors: Pascale Cossart Source Type: research
