Results 271 to 280 of about 532,868 (319)

Discrepancy Between Radiographic and Pathological Response Assessment in Neoadjuvant Treatment for Pancreatic Cancer: A Comparison Between Neoadjuvant Chemotherapy and Neoadjuvant Chemoradiotherapy

Annals of Gastroenterological Surgery, EarlyView.
This study investigated the association between radiographic and pathological response assessments among patients with pancreatic cancer receiving neoadjuvant chemotherapy and neoadjuvant chemoradiotherapy. The present study revealed that discrepancies between radiographic and histological responses may differ according to the type of therapy ...
Satoru Miyahara, Hidenori Takahashi, Yoshito Tomimaru, Shogo Kobayashi, Daisaku Yamada, Hirofumi Akita, Takehiro Noda, Yuichiro Doki, Hidetoshi Eguchi   +8 more
wiley   +1 more source

Iterative refinement and goal articulation to optimize large language models for clinical information extraction. [PDF]

NPJ Digit Med
Hein D, Christie A, Holcomb M, Xie B, Jain AJ, Vento J, Rakheja N, Shakur AH, Christley S, Cowell LG, Brugarolas J, Jamieson AR, Kapur P.   +12 more
europepmc   +1 more source

Association Between the Degree of Arterial Contact Angle and Survival in Patients With Borderline Resectable Pancreatic Cancer

Annals of Gastroenterological Surgery, EarlyView.
This study evaluated the prognostic impact of arterial contact angle in patients with borderline resectable pancreatic cancer undergoing neoadjuvant therapy and surgery. An arterial contact angle of ≥ 90° was associated with higher R1 resection rates and worse survival outcomes.
Katsuhisa Ohgi, Ryo Ashida, Mihoko Yamada, Shimpei Otsuka, Yoshiyasu Kato, Rui Sato, Akifumi Notsu, Katsuhiko Uesaka, Teiichi Sugiura   +8 more
wiley   +1 more source

Federated and reusable processing of Earth observation data. [PDF]

Sci Data
Mohr M, Pebesma E, Dries J, Lippens S, Janssen B, Thiex D, Milcinski G, Schumacher B, Briese C, Claus M, Jacob A, Sacramento P, Griffiths P.   +12 more
europepmc   +1 more source

Artificial Intelligence‐Guided Total Mesorectal Excision: Development of a Deep Learning Model to Identify the Pelvic Fascial Plane Anatomy

Annals of Gastroenterological Surgery, EarlyView.
AI‐based segmentation of fascial planes during total mesorectal excision. Ground truth and predicted boundaries are compared for mesorectal fascia (blue), parietal pelvic fascia (red), and the holy plane (white). ABSTRACT Aim Total mesorectal excision entails dissection of the “holy plane” without damaging the mesorectal and parietal pelvic fasciae. We
Yuta Suzuki, Daichi Kitaguchi, Norihito Kosugi, Hiro Hasegawa, Nobuyoshi Takeshita, Yusuke Kinugasa, Masaaki Ito   +6 more
wiley   +1 more source

Learnable and Expressive Visualization Authoring Through Blended Interfaces. [PDF]

IEEE Trans Vis Comput Graph
L'Yi S, van den Brandt A, Adams E, Nguyen HN, Gehlenborg N.   +4 more
europepmc   +1 more source

Machine Learning for Organic Fluorescent Materials

Aggregate, EarlyView.
Organic fluorescent materials (OFMs) have demonstrated significant potential in diverse applications. Conventional approaches for studying OFMs face significant limitations in fluorescence spectroscopy and computational methods. Machine learning (ML) has revolutionized materials chemistry, offering superior predictive accuracy and efficiency over ...
Jiamin Zhong, Wei Zhu, Shoutao Shen, Nan Zhou, Meiyang Xi, Kui Du, Dong Wang, Ben Zhong Tang   +7 more
wiley   +1 more source

Who Reads the Trade Marks Register? [PDF]

Oxf J Leg Stud
Burrell R, Handler M.
europepmc   +1 more source

In Situ Graph Reasoning and Knowledge Expansion Using Graph‐PRefLexOR

Advanced Intelligent Discovery, EarlyView.
Graph‐PRefLexOR is a novel framework that enhances language models with in situ graph reasoning, symbolic abstraction, and recursive refinement. By integrating graph‐based representations into generative tasks, the approach enables interpretable, multistep reasoning.
Markus J. Buehler
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source