Literature DB >> 32076657

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

Mohammadhadi Khorrami1, Monica Khunger1, Alexia Zagouras1, Pradnya Patil1, Rajat Thawani1, Kaustav Bera1, Prabhakar Rajiah1, Pingfu Fu1, Vamsidhar Velcheti1, Anant Madabhushi1.   

Abstract

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in predicting (a) time to progression (TTP) and overall survival (OS) as well as (b) response to chemotherapy in patients with non-small cell lung cancer (NSCLC).
MATERIALS AND METHODS: Data in a total of 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy at Cleveland Clinic were retrospectively analyzed. The patients were divided randomly into two sets with the constraint that there were an equal number of responders and nonresponders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients. A machine learning classifier trained with radiomic texture features extracted from intra- and peritumoral regions of non-contrast-enhanced CT images was used to predict response to chemotherapy. The radiomic risk-score signature was generated by using least absolute shrinkage and selection operator with the Cox regression model; association of the radiomic signature with TTP and OS was also evaluated.
RESULTS: A combination of radiomic features in conjunction with a quadratic discriminant analysis classifier yielded a mean maximum area under the receiver operating characteristic curve (AUC) of 0.82 ± 0.09 (standard deviation) in the training set and a corresponding AUC of 0.77 in the independent testing set. The radiomics signature was also significantly associated with TTP (hazard ratio [HR], 2.8; 95% confidence interval [CI]: 1.95, 4.00; P < .0001) and OS (HR, 2.35; 95% CI: 1.41, 3.94; P = .0011). Additionally, decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics signature had a higher overall net benefit in prediction of high-risk patients to receive treatment than the clinicopathologic measurements.
CONCLUSION: This study suggests that radiomic texture features extracted from within and around the nodule on baseline CT scans are (a) predictive of response to chemotherapy and (b) associated with TTP and OS for patients with NSCLC.© RSNA, 2019Supplemental material is available for this article. 2019 by the Radiological Society of North America, Inc.

Entities:  

Year:  2019        PMID: 32076657      PMCID: PMC6515986          DOI: 10.1148/ryai.2019180012

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN: 2638-6100


  44 in total

1.  Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer.

Authors:  Thida Win; Kenneth A Miles; Sam M Janes; Balaji Ganeshan; Manu Shastry; Raymondo Endozo; Marie Meagher; Robert I Shortman; Simon Wan; Irfan Kayani; Peter J Ell; Ashley M Groves
Journal:  Clin Cancer Res       Date:  2013-05-09       Impact factor: 12.531

Review 2.  Lung cancer: epidemiology, etiology, and prevention.

Authors:  Charles S Dela Cruz; Lynn T Tanoue; Richard A Matthay
Journal:  Clin Chest Med       Date:  2011-12       Impact factor: 2.878

3.  Endothelial progenitor cells are associated with response to chemotherapy in human non-small-cell lung cancer.

Authors:  Ryo Morita; Kazuhiro Sato; Mariko Nakano; Hajime Miura; Hidesato Odaka; Kiyoshi Nobori; Toshimitsu Kosaka; Masaaki Sano; Hiroyuki Watanabe; Takanobu Shioya; Hiroshi Ito
Journal:  J Cancer Res Clin Oncol       Date:  2011-09-17       Impact factor: 4.553

Review 4.  Factors influencing a specific pathologic diagnosis of non-small-cell lung carcinoma.

Authors:  Jeffrey A Sulpher; Scott P Owen; Henrique Hon; Kimberly Tobros; Frances A Shepherd; Elham Sabri; Marcio Gomes; Harman Sekhon; Geoffrey Liu; Christina M Canil; Paul Wheatley-Price
Journal:  Clin Lung Cancer       Date:  2013-01-04       Impact factor: 4.785

Review 5.  HIF-1 and tumor progression: pathophysiology and therapeutics.

Authors:  Gregg L Semenza
Journal:  Trends Mol Med       Date:  2002       Impact factor: 11.951

6.  Evaluation of extratumoral lymphatic permeation in non-small cell lung cancer as a means of predicting outcome.

Authors:  Takamoto Saijo; Genichiro Ishii; Atsushi Ochiai; Takahiro Hasebe; Junji Yoshida; Mitsuyo Nishimura; Kanji Nagai
Journal:  Lung Cancer       Date:  2006-11-28       Impact factor: 5.705

7.  Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.

Authors:  Florent Tixier; Catherine Cheze Le Rest; Mathieu Hatt; Nidal Albarghach; Olivier Pradier; Jean-Philippe Metges; Laurent Corcos; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2011-02-14       Impact factor: 10.057

8.  Invasion of blood vessels as significant prognostic factor in radically resected T1-3N0M0 non-small-cell lung cancer.

Authors:  S Gabor; H Renner; H Popper; U Anegg; O Sankin; V Matzi; J Lindenmann; F M Smolle Jüttner
Journal:  Eur J Cardiothorac Surg       Date:  2004-03       Impact factor: 4.191

Review 9.  Thymidylate Synthase as a Predictive Biomarker for Pemetrexed Response in NSCLC.

Authors:  Ali A Bukhari; Ranjit K Goudar
Journal:  Lung Cancer Int       Date:  2013-12-25

Review 10.  Tumour heterogeneity and the evolution of polyclonal drug resistance.

Authors:  Rebecca A Burrell; Charles Swanton
Journal:  Mol Oncol       Date:  2014-07-10       Impact factor: 6.603

View more
  27 in total

1.  We All Need a Little Magic.

Authors:  Charles E Kahn
Journal:  Radiol Artif Intell       Date:  2019-07-31

2.  Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas?

Authors:  Amandine Crombé; François Le Loarer; Maxime Sitbon; Antoine Italiano; Eberhard Stoeckle; Xavier Buy; Michèle Kind
Journal:  Eur Radiol       Date:  2020-01-17       Impact factor: 5.315

Review 3.  The Emerging Role of Radiomics in COPD and Lung Cancer.

Authors:  Turkey Refaee; Guangyao Wu; Abdallah Ibrahim; Iva Halilaj; Ralph T H Leijenaar; William Rogers; Hester A Gietema; Lizza E L Hendriks; Philippe Lambin; Henry C Woodruff
Journal:  Respiration       Date:  2020-01-28       Impact factor: 3.580

4.  Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.

Authors:  Mohammadhadi Khorrami; Kaustav Bera; Patrick Leo; Pranjal Vaidya; Pradnya Patil; Rajat Thawani; Priya Velu; Prabhakar Rajiah; Mehdi Alilou; Humberto Choi; Michael D Feldman; Robert C Gilkeson; Philip Linden; Pingfu Fu; Harvey Pass; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Lung Cancer       Date:  2020-02-26       Impact factor: 5.705

5.  MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.

Authors:  Haimei Chen; Xiao Zhang; Xiaohong Wang; Xianyue Quan; Yu Deng; Ming Lu; Qingzhu Wei; Qiang Ye; Quan Zhou; Zhiming Xiang; Changhong Liang; Wei Yang; Yinghua Zhao
Journal:  Eur Radiol       Date:  2021-03-30       Impact factor: 5.315

6.  The Pursuit of Generalizability to Enable Clinical Translation of Radiomics.

Authors:  Pallavi Tiwari; Ruchika Verma
Journal:  Radiol Artif Intell       Date:  2020-12-16

7.  Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.

Authors:  Mohammadhadi Khorrami; Kaustav Bera; Rajat Thawani; Prabhakar Rajiah; Amit Gupta; Pingfu Fu; Philip Linden; Nathan Pennell; Frank Jacono; Robert C Gilkeson; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Eur J Cancer       Date:  2021-03-17       Impact factor: 9.162

Review 8.  The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.

Authors:  Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault
Journal:  Front Oncol       Date:  2021-05-05       Impact factor: 6.244

9.  Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma.

Authors:  Guixue Liu; Zhihan Xu; Yaping Zhang; Beibei Jiang; Lu Zhang; Lingyun Wang; Geertruida H de Bock; Rozemarijn Vliegenthart; Xueqian Xie
Journal:  Front Oncol       Date:  2021-06-23       Impact factor: 6.244

Review 10.  Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Authors:  Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba
Journal:  Eur J Hybrid Imaging       Date:  2020-12-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.