Abstract
Background:
Muscle depletion negatively impacts treatment efficacy and survival rates in cancer. Prevention and timely treatment of muscle loss require prediction of patients at risk. We aimed to investigate the potential of skeletal muscle radiomic features to predict future muscle loss.
Methods:
A total of 116 patients with stage IV non-small cell lung cancer included in a randomised controlled trial (NCT01171170) studying the effect of nitroglycerin added to paclitaxel-carboplatin-bevacizumab were enrolled. In this post hoc analysis, muscle cross-sectional area and radiomic features were extracted from computed tomography images obtained before initiation of chemotherapy and shortly after administration of the second cycle. For internal cross-validation, the cohort was randomly split in a training set and validation set 100 times. We used least absolute shrinkage and selection operator method to select features that were most significantly associated with muscle loss and an area under the curve (AUC) for model performance.
Results:
Sixty-nine patients (59%) exhibited loss of skeletal muscle. One hundred ninety-three features were used to construct a prediction model for muscle loss. The average AUC was 0.49 (95% confidence interval [CI]: 0.36, 0.62). Differences in intensity and texture radiomic features over time were seen between patients with and without muscle loss.
Conclusions:
The present study shows that skeletal muscle radiomics did not predict future muscle loss during chemotherapy in non-small cell lung cancer. Differences in radiomic features over time might reflect myosteatosis. Future imaging analysis combined with muscle tissue analysis in patients and in experimental models is needed to unravel the biological processes linked to the radiomic features.
| Original language | English |
|---|---|
| Pages (from-to) | 107-113 |
| Number of pages | 7 |
| Journal | European Journal of Cancer |
| Volume | 120 |
| DOIs | |
| Publication status | Published - Oct 2019 |
Bibliographical note
Funding Information:E.E.C.d.J., K.J.C.S., T.M.D., W.v.E., A.J., J.E.v.T., J.H.R.J.D. and A.M.W.J.S. declare that they have no conflict of interest. R.T.H.L. reports personal fees and other from OncoRadiomics SA, outside the submitted work. In addition, he has a patent EP3207521A1. A-M.C.D. reports personal fees from Roche, Boehringer Ingelheim, Eli Lily, Takeda and BMS, outside the submitted work. P.L. reports grants/sponsored research agreements from Oncoradiomics and ptTheragnostic outside the submitted work, from Health Innovation Ventures, DualTpharma and ptTheragnostics. He received an advisor (SAB)/presenter fee and/or reimbursement of travel costs/external grant writing fee and/or in kind manpower contribution from Oncoradiomics, outside the submitted work, from BHV and Convert pharmaceuticals. P.L. has shares in the company Oncoradiomics and, outside the submitted work, Convert pharmaceuticals and is the co-inventor of two patents on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licenced to Oncoradiomics and outside the submitted work, one patent on mtDNA (PCT/EP2014/059089) licenced to ptTheragnostic/DNAmito, three non-patentable inventions (softwares), licenced to ptTheragnostic/DNAmito, Oncoradiomics and Health Innovation Ventures. This study is performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, no 694812 – Hypoximmuno). This research is also supported by the Dutch Technology Foundation STW (grant no P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from SME Phase 2 (RAIL – no 673780), EUROSTARS (DART), the European Program H2020-2015-17 (ImmunoSABR - no 733008, PREDICT – ITN - no 766276), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY”- no UM 2017–8295) and Interreg V-A Euregio Meuse-Rhine (“Euradiomics”).
Funding Information:
Authors acknowledge financial support from ERC advanced grant ( ERC-ADG-2015 , no 694812 – Hypoximmuno). This research is also supported by the Dutch Technology Foundation STW (grant no P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs . Authors also acknowledge financial support from SME Phase 2 ( RAIL – no 673780 ), EUROSTARS (DART), the European Program H2020-2015-17 ( ImmunoSABR - no 733008 , PREDICT – ITN - no 766276 ), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY”- no UM 2017–8295 ) and Interreg V-A Euregio Meuse-Rhine (“Euradiomics”).
Publisher Copyright:
© 2019 The Authors
