Abstract
Objectives Detection of residual oesophageal cancer after neoadjuvant chemoradiotherapy (nCRT) is important to guide treatment decisions regarding standard oesophagectomy or active surveillance. The aim was to validate previously developed 18F-FDG PET-based radiomic models to detect residual local tumour and to repeat model development (i.e. 'model extension') in case of poor generalisability. Methods This was a retrospective cohort study in patients collected from a prospective multicentre study in four Dutch institutes. Patients underwent nCRT followed by oesophagectomy between 2013 and 2019. Outcome was tumour regression grade (TRG) 1 (0% tumour) versus TRG 2-3-4 (≥1% tumour). Scans were acquired according to standardised protocols. Discrimination and calibration were assessed for the published models with optimism-corrected AUCs >0.77. For model extension, the development and external validation cohorts were combined. Results Baseline characteristics of the 189 patients included [median age 66 years (interquartile range 60-71), 158/189 male (84%), 40/189 TRG 1 (21%) and 149/189 (79%) TRG 2-3-4] were comparable to the development cohort. The model including cT stage plus the feature 'sum entropy' had best discriminative performance in external validation (AUC 0.64, 95% confidence interval 0.55-0.73), with a calibration slope and intercept of 0.16 and 0.48 respectively. An extended bootstrapped LASSO model yielded an AUC of 0.65 for TRG 2-3-4 detection. Conclusion The high predictive performance of the published radiomic models could not be replicated. The extended model had moderate discriminative ability. The investigated radiomic models appeared inaccurate to detect local residual oesophageal tumour and cannot be used as an adjunct tool for clinical decision-making in patients.
| Original language | English |
|---|---|
| Pages (from-to) | 709-718 |
| Number of pages | 10 |
| Journal | Nuclear Medicine Communications |
| Volume | 44 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2023 |
Bibliographical note
Funding Information:M.J.V. and J.J.B.v.L. disclose scientific grants from the KWF Dutch Cancer Society (funding of the SANO trial, project number 10825, and the preSANO trial, project number EMCR-2014-7430) and The Netherlands Organisation for Health Research and Development (ZonMw) (funding of the SANO trial, project number 843004104). P.L. discloses, within and outside the submitted work, grants/sponsored research agreements from Radiomics SA, ptTheragnostic/DNAmito and Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursement of travel costs/consultancy fee and/or in-kind manpower contribution from Radiomics SA, BHV, Merck, Varian, Elekta, ptTheragnostic, BMS and Convert Pharmaceuticals. P.L. has minority shares in the companies Radiomics SA, Convert Pharmaceuticals, Comunicare and LivingMed Biotech, and he is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728), licensed to Radiomics SA, and one issued patent on mtDNA (PCT/EP2014/059089), licensed to ptTheragnostic/DNAmito, three non-patented inventions (softwares) licensed to ptTheragnostic/DNAmito, Radiomics SA and Health Innovation Ventures and three non-issued, non-licensed patents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889). He confirms that none of the above entities or funding sources were involved in the preparation of this article. H.C.W. discloses minority shares in the company Radiomics SA, unrelated to this work. For the remaining authors, there are no conflicts of interest.
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