Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Sun, Roger; Henry, Théophraste; Laville, Adrien; Carré, Alexandre; Hamaoui, Anthony; Bockel, S.; Chaffai, I.; Lévy, Antonin; Chargari, Cyrus; Robert, Charlotte; Deutsch, Éric

doi:10.1136/jitc-2022-004848

Cited by 18 publications

(9 citation statements)

References 145 publications

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“… 29 Finally, the use of this radiomic score may serve in a clinical trial to guide localized ablative treatment on the most at-risk lesions such as stereotactic radiation therapy, shifting the average infiltration of the rest of the lesions upwards to potentially maximize the benefit of systemic immunotherapy. 30 …”

Section: Discussionmentioning

confidence: 99%

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Sun

Lerousseau

Briend-Diop

et al. 2022

J Immunother Cancer

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PurposeWhile there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study were to evaluate whether a previously validated radiomics signature of CD8 T-cells could predict progressions at a lesion level and whether the spatial heterogeneity of this radiomics score could be used at a patient level to assess the clinical response and survival of melanoma patients.MethodsClinical data from patients with advanced melanoma treated in our center with immunotherapy were retrieved. Radiomic features were extracted and the CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion size of 20% or more. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of interlesion heterogeneity on patient’s response. Fine-tuned cut-offs for predicting overall survival were evaluated after splitting data into training and test sets.ResultsA total of 136 patients were included in this study, with 1120 segmented lesions at baseline, and 1052 lesions at first evaluation. A low CD8 radiomics score at baseline was associated with a significantly higher risk of lesion progression (AUC=0.55, p=0.0091), especially for lesions larger than >1 mL (AUC=0.59 overall, p=0.0035, with AUC=0.75, p=0.002 for subcutaneous lesions, AUC=0.68, p=0.01, for liver lesions and AUC=0.62, p=0.03 for nodes). The least infiltrated lesion according to the radiomics score of CD8 T-cells was positively associated with overall survival (training set HR=0.31, p=0.00062, test set HR=0.28, p=0.016), which remained significant in a multivariate analysis including clinical and biological variables.ConclusionsThese results confirm the predictive value at a lesion level of the biologically inspired CD8 radiomics score in melanoma patients treated with anti-PD1-based immunotherapy and may be interesting to assess the disease spatial heterogeneity to evaluate the patient prognosis with potential clinical implication such as tumor selection for focal ablative therapies.

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Section: Discussionmentioning

confidence: 99%

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Sun

Lerousseau

Briend-Diop

et al. 2022

J Immunother Cancer

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“…Techniques such as radiolabelled anti-CD8 imaging and radiomics-based AI provide valuable insights into the immune microenvironment and enable the extraction of quantitative features from medical images, enhancing the ability to comprehensively understand tumor heterogeneity. 98 These innovative approaches not only could guide clinicians toward more effective and targeted interventions in precision medicine but also highlight the importance of addressing the limitations of traditional biopsy methods. In the realm of combining radiotherapy and immunotherapy, imaging biomarkers and radiomics offer noninvasive tools for assessing the entire disease, including its spatial heterogeneity and temporal changes.…”

Section: Discussionmentioning

confidence: 99%

Principles of risk assessment in colon cancer: immunity is key

Hijazi,

Galon

2024

OncoImmunology

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In clinical practice, the administration of adjuvant chemotherapy (ACT) following tumor surgical resection raises a critical dilemma for stage II colon cancer (CC) patients. The prognostic features used to identify high-risk CC patients rely on the pathological assessment of tumor cells. Currently, these factors are considered for stratifying patients who may benefit from ACT at early CC stages. However, the extent to which these factors predict clinical outcomes ( i.e . recurrence, survival) remains highly controversial, also uncertainty persists regarding patients’ response to treatment, necessitating further investigation. Therefore, an imperious need is to explore novel biomarkers that can reliably stratify patients at risk, to optimize adjuvant treatment decisions. Recently, we evaluated the prognostic and predictive value of Immunoscore (IS), an immune digital-pathology assay, in stage II CC patients. IS emerged as the sole significant parameter for predicting disease-free survival (DFS) in high-risk patients. Moreover, IS effectively stratified patients who would benefit most from ACT based on their risk of recurrence, thus predicting their outcomes. Notably, our findings revealed that digital IS outperformed the visual quantitative assessment of the immune response conducted by expert pathologists. The latest edition of the WHO classification for digestive tumor has introduced the evaluation of the immune response, as assessed by IS, as desirable and essential diagnostic criterion. This supports the revision of current cancer guidelines and strongly recommends the implementation of IS into clinical practice as a patient stratification tool, to guide CC treatment decisions. This approach may provide appropriate personalized therapeutic decisions that could critically impact early-stage CC patient care.

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“…To describe TIME to predict response effect, radiomics signature may also be adopted . A study by Sun et al 61 applied the radiomics-based CD8-T cells score on multiple lesions on the baseline CT scan of 94 solid cancer patients treated with immunotherapy and radiotherapy and found the score at baseline of responding lesions was higher than those of stable or progressive lesions (AUC = 0.63, 95%CI 0.56 to 0.71), p = 0.0020). Korpics et al also had a similar conclusion (HR = 0.18; 95%CI 0.04 to 0.74; p = 0.018).…”

Section: Radiomic Studies Regarding the Tme Reveal Crucial Biomarkers...mentioning

confidence: 99%

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence—the Novel Biomarkers in Breast Cancer Immune Microenvironment

Lin,

Wang,

et al. 2023

Technol Cancer Res Treat

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Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.

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Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Cited by 18 publications

References 145 publications

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Principles of risk assessment in colon cancer: immunity is key

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence—the Novel Biomarkers in Breast Cancer Immune Microenvironment

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