REFERENCES

1. Jaffe CC. Response assessment in clinical trials: implications for sarcoma clinical trial design. Oncologist 2008;13:14-8.

2. Ko CC, Yeh LR, Kuo YT, Chen JH. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res 2021;9:52.

3. Therasse P. Measuring the clinical response: what does it mean? Eur J Cancer 2002;38:1817-23.

4. Palmer MK. WHO handbook for reporting results of cancer treatment. Br J Cancer 1982;45:484-5.

5. Nishino M, Jagannathan JP, Ramaiya NH, Van den Abbeele AD. Revised RECIST guideline version 1.1: what oncologists want to know and what radiologists need to know. AJR Am J Roentgenol 2010;195:281-9.

6. Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. Breast Cancer 2005;12:S16-27.

7. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228-47.

8. Fanciullo C, Gitto S, Carlicchi E, Albano D, Messina C, Sconfienza LM. Radiomics of musculoskeletal sarcomas: a narrative review. J Imaging 2022;8:45.

9. Benjamin RS, Choi H, Macapinlac HA, et al. We should desist using RECIST, at least in GIST. J Clin Oncol 2007;25:1760-4.

10. Choi H, Charnsangavej C, Faria SC, et al. Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol 2007;25:1753-9.

11. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.

12. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-6.

13. Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys 2020;47:e185-202.

14. Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys 2014;41:050902.

15. Reiazi R, Abbas E, Famiyeh P, et al. The impact of the variation of imaging parameters on the robustness of computed tomography radiomic features: a review. Comput Biol Med 2021;133:104400.

16. Cardenas CE, Yang J, Anderson BM, Court LE, Brock KB. Advances in auto-segmentation. Semin Radiat Oncol 2019;29:185-97.

17. Tian F, Hayano K, Kambadakone AR, Sahani DV. Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion. Abdom Imaging 2015;40:1705-12.

18. Escobar T, Vauclin S, Orlhac F, et al. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Med Phys 2022;49:3816-29.

19. Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015;60:5471-96.

20. Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019;50:1773-88.

21. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749-62.

22. Nardone V, Reginelli A, Grassi R, et al. Delta radiomics: a systematic review. Radiol Med 2021;126:1571-83.

23. Crombé A, Périer C, Kind M, et al. T2-based MRI delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging 2019;50:497-510.

24. Lin P, Yang PF, Chen S, et al. A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 2020;20:7.

25. Gao Y, Kalbasi A, Hsu W, et al. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Phys Med Biol 2020;65:175006.

26. Sanduleanu S, Woodruff HC, de Jong EEC, et al. Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 2018;127:349-60.

27. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295:328-38.

28. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104-7.

29. Shaikh FA, Kolowitz BJ, Awan O, et al. Technical challenges in the clinical application of radiomics. JCO Clin Cancer Inform 2017;1:1-8.

30. Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 2020;11:91.

31. Ibrahim A, Primakov S, Beuque M, et al. Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework. Methods 2021;188:20-9.

32. Tang C, Hobbs B, Amer A, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep 2018;8:1922.

33. Tunali I. Hypoxia-related radiomics predict immunotherapy response: a multi-cohort study of NSCLC. bioRxiv 2020:020859.

34. Tinoco G, Husain M, Hoyd R, et al. The sarcoma microbiome as a diagnostic and therapeutic target. JCO 2021;39:11541.

35. Madanat-Harjuoja LM, Klega K, Lu Y, et al. Circulating tumor DNA is associated with response and survival in patients with advanced leiomyosarcoma. Clin Cancer Res 2022;28:2579-86.

Journal of Cancer Metastasis and Treatment
ISSN 2454-2857 (Online) 2394-4722 (Print)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/