A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

Sun, Roger; Limkin, Elaine Johanna; Vakalopoulou, Maria; Dercle, Laurent; Champiat, Stéphane; Han, Shan Rong; Verlingue, Loïc; Brandão, David; Lancia, Andrea; Ammari, Samy; Hollebecque, Antoine; Scoazec, Jean‐Yves; Marabelle, Aurélien; Massard, Christophe; Soria, Jean‐Charles; Robert, Charlotte; Paragios, Nikos; Deutsch, Éric; Ferté, Charles

doi:10.1016/s1470-2045(18)30413-3

Cited by 853 publications

(629 citation statements)

References 48 publications

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“…Most radiomics studies use features extracted from the whole tumor for analysis, which undoubtedly can reflect the heterogeneity of cancer. However, literature studies have indicated that features derived from the peripheral region of the tumor can provide additional information about the tumor microenvironment . Because tissue patterns of the bladder wall region on MRI will change when the bladder tumor invades the muscle layer, whether the radiomics features extracted from the basal part of the tumor can be used for determining muscular invasiveness is an interesting problem that warrants investigation .…”

Section: Discussionmentioning

confidence: 99%

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Zheng

Kong

et al. 2019

Cancer

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Background Bladder cancer (BCa) can be divided into muscle‐invasive BCa (MIBC) and non−muscle‐invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic‐clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC. Methods In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2‐weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic‐clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69). Results The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging‐determined tumor size, the radiomic‐clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram. Conclusions The proposed noninvasive radiomic‐clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.

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Section: Discussionmentioning

confidence: 99%

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Zheng

Kong

et al. 2019

Cancer

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“…Because early stages are often curable, this could drastically improve patient outcomes, minimize overtreatment, and even save lives. Furthermore, AI also can enhance lung cancer staging and characterization for treatment selection, as well as monitoring treatment response (Table ) …”

Section: Lung Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,030

721

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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“…Radiomics is an imaging processing and analysis technique that enables the conversion of routine radiological images into quantitative data and the subsequent mining of high‐dimensional data . The assumption for radiomics is that biomedical imaging can reflect both macroscopic and pathophysiological characteristics of tissues, which can be uncovered by quantitative image analyses . The ultimate goal of radiomics is to generate imaging‐based biomarkers to improve knowledge of tumor biological behaviors and advance the development of precision medicine …”

Section: Imaging Biomarkers For Immune Checkpoint Therapymentioning

confidence: 99%

“…Currently, conventional computed tomography (CT) and magnetic resonance (MR) imaging are widely employed for the measurement of tumor burden by directly revealing morphological changes in target lesions . Furthermore, several emerging imaging modalities, including metabolic imaging, functional MR imaging, radiomics and molecular imaging, can provide important information regarding tumor biological behaviors and peritumoral microenvironment . Accordingly, imaging‐based biomarkers have shown tremendous potential to predict clinical responses and prognosis of cancer patients receiving ICB therapy.…”

Section: Introductionmentioning

confidence: 99%

Role of medical imaging for immune checkpoint blockade therapy: From response assessment to prognosis prediction

2019

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Immune checkpoint blockade (ICB) represents a promising approach in cancer therapy. Owing to the peculiar biologic mechanisms of anticancer activity, checkpoint blockers are accompanied with distinctive response patterns and toxicity profiles. Medical imaging is the cornerstone for response assessment to immunotherapy and plays a critical role in monitoring of immune‐related adverse events (irAEs). Imaging‐based biomarkers have shown tremendous potential for the prediction of therapeutic efficacies and clinical outcomes in patients treated with checkpoint inhibitors. In this article, the landscape of current response assessment systems for immunotherapy was reviewed with a special focus on the latest advances in the assessment of responses to ICB. Emerging imaging biomarkers were discussed along with the challenges regarding their clinical transformation. In addition, the biological mechanisms and clinical applications of ICB and irAEs were also within the scope of this review.

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

Cited by 853 publications

References 48 publications

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Artificial intelligence in cancer imaging: Clinical challenges and applications

Role of medical imaging for immune checkpoint blockade therapy: From response assessment to prognosis prediction

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