Cited by CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients

Coupling Habitat Radiomic Analysis with the Diversification of the Tumor ecosystem: Illuminating New Strategy in the Assessment of Postoperative Recurrence of Non-Muscle Invasive Bladder Cancer DOI

Hong Li,

Yiqun Sui,

Yongli Tao

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 32(2), P. 821 - 833

Published: Oct. 24, 2024

Language: Английский

Citations

Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace DOI

Yu Xiaojian,

Qu Zhanbo,

Jian Chu

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 18, 2024

The advancements in artificial intelligence (AI) technology for image recognition were propelling molecular pathology research into a new era. To summarize the hot spots and trends field of recognition. Relevant articles from January 1st, 2010, to August 25th, 2023, retrieved Web Science Core Collection. Subsequently, CiteSpace was employed bibliometric visual analysis, generating diverse network diagrams illustrating keywords, highly cited references, topics, trends. A total 110 relevant extracted pool 10,205 articles. overall publication count exhibited rising trend each year. leading contributors terms institutions, countries, authors Maastricht University (11 articles), United States (38 Kather Jacob Nicholas (9 respectively. Half top ten based on volume, affiliated with Germany. most frequently article authored by Nicolas Coudray et al. accumulating 703 citations. keyword "Deep learning" had highest frequency 2019. Notably, highlighted keywords 2022 2023 included "microsatellite instability", there 21 focusing utilizing algorithms recognize microsatellite instability (MSI) colorectal cancer (CRC) pathological images. use DL is expected provide strategy effectively solve current problem time-consuming expensive detection. Therefore, further needed address issues, such as data quality standardization, model interpretability, resource infrastructure requirements.

Language: Английский

Citations

Predictive Modeling of Brain Metastasis in Advanced Lung Adenocarcinoma: A Hybrid Approach Combining Traditional Radiomics and Deep Learning from Thoracic CT Images DOI

Shuai Qie,

Larry E. Kun, Hongyun Shi

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 22, 2024

Abstract Purpose: Create a deep learning-based radiomics framework to anticipate prediction models for advanced lung adenocarcinoma with brain metastases. This aims inform individualized treatment and prognosis, enhancing clinical decisions patient outcomes. Methods: Analyzed 404 patients' CT scans from two hospitals. Extracted handcrafted learning features. Developed three (Rad, DTL, Combined) predict metastasis risk. The Combined model features formed the DLRN model. Evaluated using DCA Calibration Curve. Results: outperformed others, AUCs of 0.978 (training) 0.833 (validation). When combined data, achieved 0.979 0.837 (validation), high accuracy, sensitivity, specificity. showed DLRN's benefit. Conclusions: validated precise

Language: Английский

Citations

CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients DOI

Weibo Kong,

Junrui Xu,

Yunlong Huang

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Dec. 3, 2024

We used habitat radiomics as an innovative tumor biomarker to predict the outcome of neoadjuvant therapy for esophageal cancer.

Language: Английский

Citations