Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Purpose: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pre-treatment DOS functional maps for predicting LABC response to NAC. Methods: LABC patients (n = 37) underwent DOS-breast imaging before starting neoadjuvant chemotherapy. Breast-tissue parametric maps were constructed and texture analyses were performed based on grey level co-occurrence matrices (GLCM) for feature extraction. Ground-truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS-textural features computed on volumetric tumour data before the start of treatment (i.e. “pre-treatment”) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naïve Bayes, and k-nearest neighbour (k-NN) classifiers. Results: Data indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5 and 89.0%, respectively and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn/%Sp = 78.0/81.0% and an accuracy of 79.5%. Conclusions: This study demonstrated that pre-treatment tumour DOS-texture features can predict breast cancer response to NAC and potentially guide treatments

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

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets

Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

Purpose: This study comprises two subprojects. In subproject one, the study purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS) in locally advanced breast cancer (LABC) during chemotherapy. In subproject two, DOS-based functional maps were analysed with texture-based image features to predict breast cancer response before the start of NAC. Patients and Measurements: The institution’s ethics review board approved this study. For subproject one, subjects (n=22) gave written consent before participating in the study. Participants underwent non-invasive, DOS and QUS imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before surgical removal of the tumour (mastectomy and/or lumpectomy); corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumour ultrasound data using spectral analysis. In the same patients, DOS was used to measure parameters relating to tumour haemoglobin and tissue composition such as %Water and %Lipids. Discriminant analysis and receiver-operating characteristic (ROC) analyses were used to correlate the measured imaging parameters to Miller-Payne pathological response during treatment. Additionally, multivariate analysis was carried out for pairwise DOS and QUS parameter combinations to determine if an increase in the classification accuracy could be obtained using combination DOS and QUS parametric models. For subproject two, 15 additional patients we recruited after first giving their written informed consent. A pooled analysis was completed for all DOS baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients planned for NAC had functional DOS maps and associated textural features generated. A grey-level co-occurrence matrix (texture) analysis was completed for parameters associated with haemoglobin, tissue composition, and optical properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering amplitude [SA]) prior to treatment. Textural features included contrast (con), vi correlation (cor), energy (ene), and homogeneity (hom). Patients were classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological response criteria after treatment completion. In order to test if baseline univariate texture features could predict treatment response, a receiver operating characteristic (ROC) analysis was performed, and the optimal sensitivity, specificity and area under the curve (AUC) was calculated using Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to test 40 DOS-texture features and all possible bivariate combinations using a naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and validation and ROC analysis were performed to find the maximum sensitivity and specificity of bivariate DOS-texture features. Results: For subproject one, individual DOS and QUS parameters, including the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin (HbT) were significant markers for response outcome after one week of treatment (p<0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI+HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment. For subproject two, the results indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0, a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model. Conclusion: DOS and QUS demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Also, the results of this study demonstrated that DOS-texture analysis can be used to predict breast cancer response groups prior to starting NAC using baseline DOS measurements

Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients

Our goal in this research is twofold: to develop clinical performance databases of cancer patients, and to conduct data mining and machine learning studies on collected patient records. We use these studies to develop models for predicting cancer patient medical outcomes. The clinical database is developed in conjunction with surgeons and oncologists at UMass Memorial Hospital. Aspects of the database design and representation of patient narrative are discussed here. Current predictive model design in medical literature is dominated by linear and logistic regression techniques. We seek to show that novel machine learning methods can perform as well or better than these traditional techniques. Our machine learning focus for this thesis is on pancreatic cancer patients. Classification and regression prediction targets include patient survival, wellbeing scores, and disease characteristics. Information research in oncology is often constrained by type variation, missing attributes, high dimensionality, skewed class distribution, and small data sets. We compensate for these difficulties using preprocessing, meta-learning, and other algorithmic methods during data analysis. The predictive accuracy and regression error of various machine learning models are presented as results, as are t-tests comparing these to the accuracy of traditional regression methods. In most cases, it is shown that the novel machine learning prediction methods offer comparable or superior performance. We conclude with an analysis of results and discussion of future research possibilities

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub