Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Peeken, Jan C.; Neumann, Jan; Asadpour, Rebecca; Leonhardt, Yannik; Moreira, J.; Hippe, Daniel S.; Klymenko, Olena; Foreman, Sarah C.; Schacky, Claudio E. von; Spraker, Matthew B.; Schaub, Stephanie K.; Dapper, Hendrik; Knebel, Carolin; Mayr, Nina A.; Woodruff, Henry C.; Lambin, Philippe; Nyflot, Matthew J.; Gersing, Alexandra S.; Combs, Stephanie E.

doi:10.3390/cancers13081929

Cited by 34 publications

(24 citation statements)

References 64 publications

(95 reference statements)

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“…A radiomic approach may be used to improve prediction of patients’ outcome. RM texture analysis alone [ 113 , 114 ] or combined with PET/CT metabolic data [ 115 , 116 ] was associated with metastatic relapse and specific signatures were identified for prediction of survival [ 117 , 118 ]. Radiomic analysis was also applied on surveillance MRI in patients undergoing follow-up after surgical resection [ 119 ], resulting in improved detection and characterization of local recurrence [ 120 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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“…Clinicians have applied radiomics to predict patient prognosis, predominantly metastatic relapse-free survival (MFS), local relapse-free survival (LFS), overall survival (OS), and the risk of presenting lung metastases following initial treatments (either curative surgery alone or neoadjuvant radiotherapy/chemotherapy followed by curative surgery). The resulting radiomics prognostic models demonstrated good to strong performances (c-index: 0.77 for LFS, 0.84-0.93 for MFS, and 0.73-0.80 for OS) [36,[58][59][60][61][62][63][64][65]. Eventually, radiomics has demonstrated the ability to predict the histologic response following neoadjuvant treatments using baseline RFs and delta-radiomics, which correspond to a quantitative change in RFs between two radiological evaluations.…”

Section: F I G U R Ementioning

confidence: 93%

The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

Crombé

Roulleau‐Dugage

Italiano

2022

Cancer Communications

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Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring.Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings.Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides

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“…In comparison with standard biopsy techniques, radiomics analysis offers the advantage of being able to non-invasively quantify heterogeneity of entire tumor volumes at given time points of interest, which in theory should allow for better characterization of chemotherapeutic response than use of sizebased criteria alone [6, 20-22, 26, 27, 29-31]. Radiomics has already been successfully applied to a variety of clinical applications related to STS, including stratification of benign from malignant soft tissue neoplasms, prediction of histologic grade, and assessment of metastatic risk [27,[31][32][33][34], though lack of standardized protocols has hindered widespread adoption of radiomics workflows in clinical practice [16,24,25,35].…”

Section: Introductionmentioning

confidence: 99%

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

et al. 2023

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Objectives To evaluate the performance of machine learning–augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. Methods Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning–augmented radiomics analyses. Results Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22–0.58) and 0.44 (95% CI 0.26–0.62) for RF and AdaBoost, respectively. Conclusion Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

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Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Cited by 34 publications

References 64 publications

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

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