Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

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Radiomics: the bridge between medical imaging and personalized medicine

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.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21552

Artificial intelligence in cancer imaging: Clinical challenges and applications.

https://hal.inria.fr/hal-01648559/document

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

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Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n = 353) and verified them in an independent validation cohort (n = 352) All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter) We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR+ and EGFR- cases (AUC = 069) Combining this signature with a clinical model of EGFR status (AUC = 070) significantly improved prediction accuracy (AUC = 075) The highest performing signature was capable of distinguishing between EGFR+ and KRAS+ tumors (AUC = 080) and, when combined with a clinical model (AUC = 081), substantially improved its performance (AUC = 086) A KRAS+/KRAS- radiomic signature also showed significant albeit lower performance (AUC = 063) and did not improve the accuracy of a clinical predictor of KRAS status Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost Cancer Res; 77(14); 3922-30 ©2017 AACR

/pdf/somatic-mutations-drive-distinct-imaging-phenotypes-in-lung-58lin4gb49.pdf

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

https://www.jto.org/article/S1556-0864(16)30600-1/pdf

Predicting Malignant Nodules from Screening CT Scans

Low-dose helical computed tomography (LDCT) has facilitated the early detection of lung cancer through pulmonary screening of patients. There have been a few attempts to develop a computer-aided diagnosis system for classifying pulmonary nodules using size and shape, with little attention to texture features. In this work, texture and shape features were extracted from pulmonary nodules selected from the LIDC data set. Several classifiers including Decision Trees, Nearest Neighbor, and Support Vector Machines (SVM) were used for classifying malignant and benign pulmonary nodules. An accuracy of 90.91% was achieved using a 5-nearest-neighbors algorithm and a data set containing texture features only. Laws and Wavelet features received the highest rank when using feature selection implying a larger contribution in the classification process. Considering the improvement in classification accuracy, the use of texture features appears to be a promising direction in computer-aided diagnosis of pulmonary nodules in LDCT.

Henry Krewer

Papers

Predicting Malignant Nodules from Screening CT Scans

Effect of Texture Features in Computer Aided Diagnosis of Pulmonary Nodules in Low-Dose Computed Tomography