Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging

Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI

(2020) Xiao-Yan Zhang et al.   RADIOLOGY

Assessment of a Watch-and-Wait Strategy for Rectal Cancer in Patients With a Complete Response After Neoadjuvant Therapy

(2019) J. Joshua Smith et al.   JAMA Oncology

Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer

(2019) Seung Hyuck Jeon et al.   Radiation Oncology

MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

(2019) Xiaoping Yi et al.   Frontiers in Oncology

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer

(2019) R. Ferrari et al.   EUROPEAN JOURNAL OF RADIOLOGY

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI

(2019) Liming Shi et al.   MAGNETIC RESONANCE IMAGING

Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics

(2019) Philippe Bulens et al.   RADIOTHERAPY AND ONCOLOGY

MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy

(2018) Natally Horvat et al.   RADIOLOGY

Impact of Tumor Regression Grade as a Major Prognostic Factor in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Proposal for a Modified Staging System

(2018) Changhoon Song et al.   Cancers

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

(2017) Babak Ehteshami Bejnordi et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

Prospective Validation of a Low Rectal Cancer Magnetic Resonance Imaging Staging System and Development of a Local Recurrence Risk Stratification Model

(2016) Nicholas J. Battersby et al.   ANNALS OF SURGERY

Magnetic Resonance Tumor Regression Grade and Residual Mucosal Abnormality as Predictors for Pathological Complete Response in Rectal Cancer Postneoadjuvant Chemoradiotherapy

(2016) Jemma Bhoday et al.   DISEASES OF THE COLON & RECTUM

Assessment of Clinical Complete Response After Chemoradiation for Rectal Cancer with Digital Rectal Examination, Endoscopy, and MRI: Selection for Organ-Saving Treatment

(2015) Monique Maas et al.   ANNALS OF SURGICAL ONCOLOGY

Organ preservation in rectal cancer: have all questions been answered?

(2015) Corrie A M Marijnen   LANCET ONCOLOGY

Clinical Criteria Underestimate Complete Pathological Response in Rectal Cancer Treated With Neoadjuvant Chemoradiotherapy

(2014) Fraser M. Smith et al.   DISEASES OF THE COLON & RECTUM

Restaging of Locally Advanced Rectal Cancer With Magnetic Resonance Imaging and Endoluminal Ultrasound After Preoperative Chemoradiotherapy

(2014) Ri-Sheng Zhao et al.   DISEASES OF THE COLON & RECTUM

The role of diffusion-weighted MRI and 18F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: A systematic review

(2014) Ines Joye et al.   RADIOTHERAPY AND ONCOLOGY

Magnetic pulse cladding of aluminum alloy on mild steel tube

(2013) Haiping Yu et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

Preoperative High-resolution Magnetic Resonance Imaging Can Identify Good Prognosis Stage I, II, and III Rectal Cancer Best Managed by Surgery Alone

(2011) Fiona G.M Taylor et al.   ANNALS OF SURGERY

Diffusion-Weighted MRI for Selection of Complete Responders After Chemoradiation for Locally Advanced Rectal Cancer: A Multicenter Study

(2011) Doenja M. J. Lambregts et al.   ANNALS OF SURGICAL ONCOLOGY

Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data

(2010) Monique Maas et al.   LANCET ONCOLOGY