Jia Wu
@JiaWu_PhD
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Associate professor, Dept. of Imaging Physics @MDAndersonNews.Tweets are my own.
Houston, TX
Joined April 2021
RT @LeXiuning: @JiaWu_PhD Now we can predict recurrence risk in resected lung cancer using habitat imaging and ctDNA integration model. The….
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RT @LUNGevity: Provocative discussion by Dr. John Heymach @MDAndersonNews #AACR24. Local consolidation therapy (LCT) w/ surgery +/- radiati….
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RT @CellRepMed: Online now: Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept
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RT @MDAndersonNews: From a new appendix cancer treatment to improved #MultipleMyeloma outcomes to a preventive method for managing #CART ce….
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happy to share our recent study on #patterns SwarmDeepSurv, by leveraging #swarmintelligence to advance #deep survival network for prognostic signature. In #radiomics, feature #reproducibility is an important topic, especially in a…
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Come and join our 2nd workshop on Computational #mathematics #modeling in Cancer Analysis to be held in conjunction with #MICCAI 2023. The full manuscript submission deadline is 23:59, Pacific Time, July 10th, 2023.
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We'd like to thank Drs Nicola Sverzellati and Cristina Marrocchio for sharing their great insights "Deep learning-based radiomics: pacing immunotherapy in lung cancer" and Editor Rupa Sarkar for promoting our work!
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Appreciate the insightful comment from awesome researchers on our latest work!.
New Linked Comment from Nicola Sverzellati and.Cristina Marrocchio on #DeepLearning-based radiomics in #lungcancer. Read it here:
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This is a big team work in collaboration with our thoracic oncology group led by Jianjun Zhang, John Heymach, @NIVokes, TMI team led by Caroline Chung, Brett Carter, Stanford team led by Ruijiang Li, Heather Wakelee, and many other awesome researchers. Thank you all!.
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We also studied the model interpretability, and found certain hand crafted radiomics features correlates to deep learning risk score. BUT, radiomics alone can’t reach deep learning performance, suggesting AI captures additional imaging patterns beyond radiomics.
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It performs robustly in clinically meaningful subgroups as defined by PD-L1, histology, age, sex, race; 2) it complements clinicopathological and blood markers; 3) this model can be used to guide ICI-mono vs ICI-chemo.
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RT @LancetDigitalH: NEW Research on predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung #cancer by CT….
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happy to share our latest #AI #medicalimaging work published in #lancet #digitalhealth. Several key findings to highlight: 1) the #deeplearning CT model can predict #lungcancer prognosis after #immunotherapy, which also performs ro…
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#NSCLC #immunotherapy is evolving, in this #NatureCommunications paper ( lead by Natalie Vokes, Jianjun Zhang, Yanyan Lou, and me), we start to identify patients who benefits from ICI-mono, and who else needs to add chemo, based on…
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Our latest research on #AI of #medicalimaging has just been published. We identified 4 new imaging subtypes of distinct clinical and biological values across three cancer types, through an international collaboration with #stanforduniversity #MDAnderson…
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