Literature DB >> 33622277

MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas.

Tao Song1, Qian-Wen Zhang1, Shao-Feng Duan2, Yun Bian1, Qiang Hao1, Peng-Yi Xing1, Tie-Gong Wang1, Lu-Guang Chen1, Chao Ma1, Jian-Ping Lu3.   

Abstract

BACKGROUND: This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs).
METHODS: Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram.
RESULTS: SPNs occurred in younger age groups than hypo-NF-pNETs (P < 0.0001) and showed a clear preponderance in females (P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942-1.000] in the training set, 0.907 [95% CI, 0.765-1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923-1.000) and 0.920 (95% CI, 0.796-1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase.
CONCLUSION: CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.

Entities:  

Keywords:  Magnetic resonance imaging; Pancreatic neuroendocrine tumors; Radiomics; Solid pseudopapillary neoplasms of the pancreas

Mesh:

Year:  2021        PMID: 33622277      PMCID: PMC7901077          DOI: 10.1186/s12880-021-00563-x

Source DB:  PubMed          Journal:  BMC Med Imaging        ISSN: 1471-2342            Impact factor:   1.930


  1 in total

Review 1.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

  1 in total
  7 in total

Review 1.  GEP-NET radiomics: a systematic review and radiomics quality score assessment.

Authors:  Femke C R Staal; Else A Aalbersberg; Daphne van der Velden; Erica A Wilthagen; Margot E T Tesselaar; Regina G H Beets-Tan; Monique Maas
Journal:  Eur Radiol       Date:  2022-07-26       Impact factor: 7.034

Review 2.  A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.

Authors:  Yogesh Kumar; Surbhi Gupta; Ruchi Singla; Yu-Chen Hu
Journal:  Arch Comput Methods Eng       Date:  2021-09-27       Impact factor: 8.171

Review 3.  Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications.

Authors:  Kiersten Preuss; Nate Thach; Xiaoying Liang; Michael Baine; Justin Chen; Chi Zhang; Huijing Du; Hongfeng Yu; Chi Lin; Michael A Hollingsworth; Dandan Zheng
Journal:  Cancers (Basel)       Date:  2022-03-24       Impact factor: 6.639

4.  Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis.

Authors:  Derong Sun; Jianjiang Dong; Yindong Mu; Fangwei Li
Journal:  Contrast Media Mol Imaging       Date:  2022-07-19       Impact factor: 3.009

Review 5.  Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.

Authors:  Maria Elena Laino; Angela Ammirabile; Ludovica Lofino; Lorenzo Mannelli; Francesco Fiz; Marco Francone; Arturo Chiti; Luca Saba; Matteo Agostino Orlandi; Victor Savevski
Journal:  Healthcare (Basel)       Date:  2022-08-11

6.  Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Authors:  Muhammad Umar Nasir; Muhammad Zubair; Taher M Ghazal; Muhammad Farhan Khan; Munir Ahmad; Atta-Ur Rahman; Hussam Al Hamadi; Muhammad Adnan Khan; Wathiq Mansoor
Journal:  Sensors (Basel)       Date:  2022-10-02       Impact factor: 3.847

Review 7.  Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging.

Authors:  Ginevra Danti; Federica Flammia; Benedetta Matteuzzi; Diletta Cozzi; Valentina Berti; Giulia Grazzini; Silvia Pradella; Laura Recchia; Luca Brunese; Vittorio Miele
Journal:  Radiol Med       Date:  2021-08-24       Impact factor: 3.469

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.