Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study

[1]  Chencui Huang,et al.  Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients , 2023, International journal of surgery.

[2]  M. Santoni,et al.  Complete remissions following immunotherapy or immuno-oncology combinations in cancer patients: the MOUSEION-03 meta-analysis , 2023, Cancer Immunology, Immunotherapy.

[3]  C. Shao,et al.  A CT-Based Radiomics Model for Evaluating Peritoneal Cancer Index in Peritoneal Metastasis Cases: A Preliminary Study. , 2022, Academic radiology.

[4]  Kangneng Zhou,et al.  Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer , 2022, Nature Communications.

[5]  Dongxi Xiang,et al.  A retrospective study of clinicopathological characteristics and prognostic factors of Krukenberg tumor with gastric origin. , 2022, Journal of gastrointestinal oncology.

[6]  Bin Zhang,et al.  Radiomics in precision medicine for gastric cancer: opportunities and challenges , 2022, European Radiology.

[7]  Lei Wu,et al.  A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study , 2022, EClinicalMedicine.

[8]  Quan P. Ly,et al.  Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. , 2022, Journal of the National Comprehensive Cancer Network : JNCCN.

[9]  Bin Song,et al.  Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction , 2021, Frontiers in Oncology.

[10]  Yu Zhang,et al.  Risk factors predicting the occurrence of metachronous ovarian metastasis of gastric cancer , 2021, Annals of translational medicine.

[11]  D. de Biase,et al.  Novel HER2-Directed Treatments in Advanced Gastric Carcinoma: AnotHER Paradigm Shift? , 2021, Cancers.

[12]  A. Jemal,et al.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.

[13]  G. Brandi,et al.  DNA damage response alterations in gastric cancer: knocking down a new wall. , 2021, Future oncology.

[14]  Ruijiang Li,et al.  Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning , 2021, JAMA network open.

[15]  H. Xi,et al.  Establishment and validation of a nomogram to predict the risk of ovarian metastasis in gastric cancer: Based on a large cohort , 2020, World journal of clinical cases.

[16]  S. Nougaret,et al.  Krukenberg Tumors: Update on Imaging and Clinical Features. , 2020, AJR. American journal of roentgenology.

[17]  F. Giganti,et al.  Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multi-center study. , 2020, Annals of oncology : official journal of the European Society for Medical Oncology.

[18]  A. Ardizzoni,et al.  Third- and later-line treatment in advanced or metastatic gastric cancer: a systematic review and meta-analysis. , 2019, Future oncology.

[19]  Jing Lei,et al.  CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer , 2019, European Radiology.

[20]  Song Liu,et al.  Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer , 2019, European Radiology.

[21]  A. Usubutun,et al.  Imaging in secondary tumors of the ovary , 2018, Abdominal Radiology.

[22]  Qi Xu,et al.  Management Of Synchronous Krukenberg Tumors From Gastric Cancer: a Single-center Experience , 2018, Journal of Cancer.

[23]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[24]  W. Xia,et al.  Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images , 2018, Australasian Physical & Engineering Sciences in Medicine.

[25]  S. Filip,et al.  The pathogenesis, diagnosis, and management of metastatic tumors to the ovary: a comprehensive review , 2017, Clinical & Experimental Metastasis.

[26]  G. Costamagna,et al.  Krukenberg Tumors of Gastric Origin: The Rationale of Surgical Resection and Perioperative Treatments in a Multicenter Western Experience , 2016, World Journal of Surgery.

[27]  Xinhan Zhao,et al.  Clinical characteristics and prognostic analysis of Krukenberg tumor. , 2015, Molecular and clinical oncology.

[28]  Yvonne Vergouwe,et al.  Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.

[29]  W. Pei,et al.  Clinicopathologic characteristics and prognostic factors of 63 gastric cancer patients with metachronous ovarian metastasis , 2013, Cancer biology & medicine.

[30]  F. Sweep,et al.  Secondary Ovarian Malignancies: Frequency, Origin, and Characteristics , 2009, International Journal of Gynecologic Cancer.

[31]  R. Young,et al.  From Krukenberg to Today: The Ever Present Problems Posed by Metastatic Tumors in the Ovary: Part I. Historical Perspective, General Principles, Mucinous Tumors Including the Krukenberg Tumor , 2006, Advances in anatomic pathology.

[32]  T. Yoshikawa,et al.  Clinical and pathological study of gastric cancer with ovarian metastasis , 2003, International Journal of Clinical Oncology.

[33]  A. Alavi,et al.  Opportunities and Challenges , 1998, In Vitro Diagnostic Industry in China.