Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Radiomics in [¹⁸F]FDG PET/CT:A leap in the dark?

Positron emission tomography (PET) imaging with the non-metabolisable glucose analogue 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), combined with low dose computed tomography (CT) for anatomical reference, is an important tool to detect and stage cancer or active inflammations. Visual interpretation of PET/CT images consists of (qualitative) assessment of radiotracer uptake in different tissues and their density. Furthermore, the location, size, shape, and relation with surrounding tissues of these lesions provide important clues on their nature. Yet, medical images contain much more information about tissue biology hidden in the myriad of voxels of both lesions and healthy tissue than can be assessed visually. Quantification of radiotracer uptake heterogeneity and other tissue characteristics is studied in the field of radiomics. Radiomics is a form of medical image processing that aims to find stable and clinically relevant image-derived biomarkers for lesion characterisation, prognostic stratification, and response prediction, thereby contributing to precision medicine. Radiomics consists of the conversion of (parts of) medical images into a high-dimensional set of quantitative features and the subsequent mining of this dataset for potential information useful for the quantification or monitoring of tumour or disease characteristics in clinical practice. This thesis contributed to a deeper understanding of the methodological aspects of handcrafted radiomics in [18F]FDG PET/CT, specifically in small datasets. However, most radiomic papers present proof-of-concept studies and clinical implementation is still far away. At some point in the future, radiomic biomarkers may be used in clinical practice, but at the moment we should acknowledge the limitations of the field and try to overcome these. Only then, we will be able to cross the translational gap towards clinical readiness. Future research should focus on standardisation of feature selection, model building, and ideally a tool that implements these aspects. In such a way, radiomics may redeem the promise of bringing forth imaging biomarkers that contribute to precision medicine.<br/

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming yearsThis research was partially funded by DTS17/00138 (Instituto de Salud Carlos III) and ED431F 2017/04 project (GAIN-Xunta de Galicia)S

Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology

Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy. Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patients’ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on ‘clean’ and ‘contaminated’ datasets. The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction. Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes. Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liver’s Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well. Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd

Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63-0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis

Heterogeneidad tumoral en imágenes PET-CT

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Estructura de la Materia, Física Térmica y Electrónica, leída el 28/01/2021Cancer is a leading cause of morbidity and mortality [1]. The most frequent cancers worldwide are non–small cell lung carcinoma (NSCLC) and breast cancer [2], being their management a challenging task [3]. Tumor diagnosis is usually made through biopsy [4]. However, medical imaging also plays an important role in diagnosis, staging, response to treatment, and recurrence assessment [5]. Tumor heterogeneity is recognized to be involved in cancer treatment failure, with worse clinical outcomes for highly heterogeneous tumors [6,7]. This leads to the existence of tumor sub-regions with different biological behavior (some more aggressive and treatment-resistant than others) [8-10]. Which are characterized by a different pattern of vascularization, vessel permeability, metabolism, cell proliferation, cell death, and other features, that can be measured by modern medical imaging techniques, including positron emission tomography/computed tomography (PET/CT) [10-12]. Thus, the assessment of tumor heterogeneity through medical images could allow the prediction of therapy response and long-term outcomes of patients with cancer [13]. PET/CT has become essential in oncology [14,15] and is usually evaluated through semiquantitative metabolic parameters, such as maximum/mean standard uptake value (SUVmax, SUVmean) or metabolic tumor volume (MTV), which are valuables as prognostic image-based biomarkers in several tumors [16-17], but these do not assess tumor heterogeneity. Likewise, fluorodeoxyglucose (18F-FDG) PET/CT is important to differentiate malignant from benign solitary pulmonary nodules (SPN), reducing so the number of patients who undergo unnecessary surgical biopsies. Several publications have shown that some quantitative image features, extracted from medical images, are suitable for diagnosis, tumor staging, the prognosis of treatment response, and long-term evolution of cancer patients [18-20]. The process of extracting and relating image features with clinical or biological variables is called “Radiomics” [9,20-24]. Radiomic parameters, such as textural features have been related directly to tumor heterogeneity [25]. This thesis investigated the relationships of the tumor heterogeneity, assessed by 18F-FDG-PET/CT texture analysis, with metabolic parameters and pathologic staging in patients with NSCLC, and explored the diagnostic performance of different metabolic, morphologic, and clinical criteria for classifying (malignant or not) of solitary pulmonary nodules (SPN). Furthermore, 18F-FDG-PET/CT radiomic features of patients with recurrent/metastatic breast cancer were used for constructing predictive models of response to the chemotherapy, based on an optimal combination of several feature selection and machine learning (ML) methods...El cáncer es una de las principales causas de morbilidad y mortalidad. Los más frecuentes son el carcinoma de pulmón de células no pequeñas (NSCLC) y el cáncer de mama, siendo su tratamiento un reto. El diagnóstico se suele realizar mediante biopsia. La heterogeneidad tumoral (HT) está implicada en el fracaso del tratamiento del cáncer, con peores resultados clínicos para tumores muy heterogéneos. Esta conduce a la existencia de subregiones tumorales con diferente comportamiento biológico (algunas más agresivas y resistentes al tratamiento); las cuales se caracterizan por diferentes patrones de vascularización, permeabilidad de los vasos sanguíneos, metabolismo, proliferación y muerte celular, que se pueden medir mediante imágenes médicas, incluida la tomografía por emisión de positrones/tomografía computarizada con fluorodesoxiglucosa (18F-FDG-PET/CT). La evaluación de la HT a través de imágenes médicas, podría mejorar la predicción de la respuesta al tratamiento y de los resultados a largo plazo, en pacientes con cáncer. La 18F-FDG-PET/CT es esencial en oncología, generalmente se evalúa con parámetros metabólicos semicuantitativos, como el valor de captación estándar máximo/medio (SUVmáx, SUVmedio) o el volumen tumoral metabólico (MTV), que tienen un gran valor pronóstico en varios tumores, pero no evalúan la HT. Asimismo, es importante para diferenciar los nódulos pulmonares solitarios (NPS) malignos de los benignos, reduciendo el número de pacientes que van a biopsias quirúrgicas innecesarias. Publicaciones recientes muestran que algunas características cuantitativas, extraídas de las imágenes médicas, son robustas para diagnóstico, estadificación, pronóstico de la respuesta al tratamiento y la evolución, de pacientes con cáncer. El proceso de extraer y relacionar estas características con variables clínicas o biológicas se denomina “Radiomica”. Algunos parámetros radiómicos, como la textura, se han relacionado directamente con la HT. Esta tesis investigó las relaciones entre HT, evaluada mediante análisis de textura (AT) de imágenes 18F-FDG-PET/CT, con parámetros metabólicos y estadificación patológica en pacientes con NSCLC, y exploró el rendimiento diagnóstico de diferentes criterios metabólicos, morfológicos y clínicos para la clasificación de NPS. Además, se usaron características radiómicas de imágenes 18F-FDG-PET/CT de pacientes con cáncer de mama recurrente/metastásico, para construir modelos predictivos de la respuesta a la quimioterapia, combinándose varios métodos de selección de características y aprendizaje automático (ML)...Fac. de Ciencias FísicasTRUEunpu

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence