Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept

Khene, Zine‐Eddine; Mathiéu, Romain; Peyronnet, B.; Kokorian, Romain; Gasmi, Anis; Khene, Fares; Rioux‐Leclercq, Nathalie; Kammerer‐Jacquet, Solène‐Florence; Shariat, Shahrokh F.; Laguerre, Brigitte; Bensalah, Karim

doi:10.1007/s00345-020-03334-5

Cited by 25 publications

(16 citation statements)

References 10 publications

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“…Renal carcinoma is one of the most common cancers is one of the most common malignant tumors in the urinary system [ 1 ]. By 2030, the number of new cases is expected to exceed 2.2 million and the number of deaths will exceed 1.1 million [ 2 ]. Hedgehog signaling pathway plays an important role in the growth and differentiation of human tissues and is critical for the body to maintain homeostasis in a physiological state [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Inhibitory Effect of Ursolic Acid on Proliferation and Migration of Renal Carcinoma Cells and Its Mechanism

Lyu

Zhang

Sun

et al. 2022

Computational Intelligence and Neuroscience

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Background. Renal carcinoma is one of the most common malignant tumors in the urinary system. Autophagy can be both activated and inhibited in renal carcinoma, and it plays a double-edged role in the development of renal carcinoma. In the early stage of cancer, autophagy can suppress tumors. In the late stage, autophagy contributes to the survival of tumor cells in an unfavorable environment, and some autophagy-related proteins P62, LC3B, and beclin-1 have become indicators of the prognosis of patients with renal carcinoma. Aim. To demonstrate that ursolic acid activates autophagy in renal carcinoma 786-O cells by inhibiting the hedgehog signaling pathway. Methods. The effect of ursolic acid on the viability of 786-O cells was determined by the MTT method; the effect of ursolic acid on the proliferation and migration of 786-O cells was examined by crystalline violet staining and scratch assay, respectively. For the study of autophagy, we firstly screened the time points. Western blot assay was used to detect the expression level of autophagic protein P62 at different time points of ursolic acid on 786-O. Then, the Cell MeterTM Autophagy Assay Kit was used to detect the effect of different doses of ursolic acid on the autophagic fluorescence intensity of 786-O cells; the Western blot method was used to detect the effect of different doses of ursolic acid on the expression levels of LC3II and P62 proteins in 786-O cells. Further, AdPlus-mCherry-GFP-LC3B adenovirus transfection was used to detect the effect of ursolic acid on the autophagic flow of 786-O cells; ursolic acid was combined with the autophagy inhibitor chloroquine (CQ) to detect the expression level of autophagy protein LC3II by Western blot. In terms of mechanism, the effect of ursolic acid on hedgehog signaling pathway-related proteins in 786-O cells was detected by Western blot. Results. Ursolic acid inhibited the activity, proliferation, and migration of 786-O cells, enhanced the fluorescence intensity of autophagosomes in 786-O cells, increased the expression level of autophagy marker protein LC3II, and inhibited the expression level of P62 in a time and dose-dependent manner; ursolic acid activated the autophagic flow in 786-O cells, which showed that ursolic acid caused the accumulation of autophagic fluorescent spots and enhanced the fluorescence intensity of autophagosomes. Ursolic acid activated the autophagic flow in 786-O cells, as evidenced by the accumulation of autophagic fluorescent spots and enhanced fluorescence intensity of autophagosomes, and the combined use of the autophagy inhibitor CQ increased the expression level of LC3II compared to ursolic acid alone; ursolic acid decreased the expression levels of PTCH1, GLI1, SMO, SHH, and c-Myc and increased the expression level of Sufu in the hedgehog signaling pathway. Conclusion. Ursolic acid activates autophagy in renal carcinoma 786-O cells, probably by inhibiting hedgehog signaling pathway activity.

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Section: Introductionmentioning

confidence: 99%

Inhibitory Effect of Ursolic Acid on Proliferation and Migration of Renal Carcinoma Cells and Its Mechanism

Lyu

Zhang

Sun

et al. 2022

Computational Intelligence and Neuroscience

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“…In particular, texture analysis features assess the spatial distribution of pixel intensity levels within an image, the obtained quantitative data reflect the image heterogeneity. To date, a role of radiomics and machine learning has been suggested in the prediction of Fuhrman grade in clear cell RCC and/or in the prediction of tumor response in metastatic RCC patients treated with nivolumab [20,21].…”

Section: Imaging Evaluation Of Therapy Response In Metastatic Rccmentioning

confidence: 99%

“…In detail, the median overall survival was 24.3 months in patients with high radiomics score, while 11.5 months in those with a low score. Furthermore, Khene et al built an artificial intelligence algorithm based on radiomics parameters extracted from pre-treatment enhancement-CT to predict tumor response in 48 metastatic RCC patients treated with nivolumab [21]. Five features were selected, and their four predictive models show high accuracy scores (K-nearest neighbor: 0.82; random forest tree: 0.7; logistic regression: 0.91; support vector machine: 0.81).…”

Section: Prognostic Evaluation In Metastatic Rcc Using Immune Checkpoint Inhibitorsmentioning

confidence: 99%

Current Imaging Evaluation of Tumor Response to Advanced Medical Treatment in Metastatic Renal-Cell Carcinoma: Clinical Implications

et al. 2021

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The present review is focused on the role of diagnostic tomographic imaging such as computed tomography and magnetic resonance imaging to assess and predict tumor response to advanced medical treatments in metastatic renal cell carcinoma (RCC) patients. In this regard, antiangiogenic agents and immune checkpoint inhibitors (ICIs) have developed as advanced treatment options replacing the conventional therapy based on interferon-alpha and interleuchin-2 which had unfavorable toxicity profile and low response rates. In clinical practice, the imaging evaluation of treatment response in cancer patients is based on dimensional changes of tumor lesions in sequential scans; in particular, Response Evaluation Criteria in Solid Tumors (RECIST) have been defined for this purpose and also applied in patients with metastatic RCC. However, these new drugs with predominant cytostatic effect make RECIST insufficient to realize an adequate response imaging evaluation. Therefore, new imaging criteria (mCHOI and iRECIST) have been proposed to assess tumor response to advanced medical treatments of metastatic RCC, they correlate better than RECIST with the progression-free survival and overall survival. Finally, a potential role of radiomics and machine learning models has been suggested to predict tumor response.

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“…In addition, the ability to assess lymphoid infiltrates by 'radiomics' requires pathological assessment of surgical specimens in order to generate a 'ground truth' relationship to inferred changes in the tumor. [5][6][7][8][9][10] Importance of tissue sampling in immunotherapy clinical trials The recognition of tumor-specific mutations (neoantigens) is central to the success of immunotherapy. [11][12][13] In addition, while tissue procurement is an obvious necessity in generating autologous tumor-infiltrating lymphocyte (TIL) treatments, enriched lymphocyte cultures may also serve as a research tool to understand the unique immunogenicity of each patient's tumor, and autologous tumor tissue can be characterized by DNA and RNA sequencing.…”

Section: Basic Science Of Biobankingmentioning

confidence: 99%

Defining best practices for tissue procurement in immuno-oncology clinical trials: consensus statement from the Society for Immunotherapy of Cancer Surgery Committee

Gastman

Agarwal

Berger

et al. 2020

J Immunother Cancer

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Immunotherapy is now a cornerstone for cancer treatment, and much attention has been placed on the identification of prognostic and predictive biomarkers. The success of biomarker development is dependent on accurate and timely collection of biospecimens and high-quality processing, storage and shipping. Tumors are also increasingly used as source material for the generation of therapeutic T cells. There have been few guidelines or consensus statements on how to optimally collect and manage biospecimens and source material being used for immunotherapy and related research. The Society for Immunotherapy of Cancer Surgery Committee has brought together surgical experts from multiple subspecialty disciplines to identify best practices and to provide consensus on how best to access and manage specific tissues for immuno-oncology treatments and clinical investigation. In addition, the committee recommends early integration of surgeons and other interventional physicians with expertise in biospecimen collection, especially in clinical trials, to optimize the quality of tissue and the validity of correlative clinical studies in cancer immunotherapy.

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Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept

Cited by 25 publications

References 10 publications

Inhibitory Effect of Ursolic Acid on Proliferation and Migration of Renal Carcinoma Cells and Its Mechanism

Inhibitory Effect of Ursolic Acid on Proliferation and Migration of Renal Carcinoma Cells and Its Mechanism

Current Imaging Evaluation of Tumor Response to Advanced Medical Treatment in Metastatic Renal-Cell Carcinoma: Clinical Implications

Defining best practices for tissue procurement in immuno-oncology clinical trials: consensus statement from the Society for Immunotherapy of Cancer Surgery Committee

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