A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules.

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs

Reducción de dimensionalidad en Machine Learning. Diagnóstico de cáncer de mama bsado en datos genómicos y de imagen

The target of the current Project consist in analyzing some of the Matching Learning techniques used in the current treatment of Big Data. It includes the study of the statistical and algebraic tools involved in the calculations, and an application to the diagnosis and clasification of breast cancer based on genomic and image data. To extract information from Big Data, the data obtained require to be pre-processed. In this Project we present different pre-processing techniques and analyze them and their impact on the resulting prediction models. Two Machine Learning Models are presented: One of them is focused on the diagnosis of breast cáncer base don image data. The second one is devoted to the classification of the different types of breast cancer and to the discovery of different patterns using genomic and proteinomic data. The two data basis are particularly convenient to present the Marchine Learning techniques analyzed in the Project and the corresponding pre-processing strategies.El objetivo del proyecto es analizar algunas técnicas de aprendizaje automático (Machine Learning) que se emplean en la actualidad para extracción de información de grandes cantidades de datos, estudiar las herramientas estadísticas y algebraicas que emplean en los cálculos, y aplicarlas al diagnóstico y clasificación de tipos de cáncer de mama. El manejo de grandes cantidades de datos requiere de un pre-procesamiento de los mismos para poder ser empleados. En este proyecto se presentan y analizan también distintas herramientas utilizadas en el pre-procesado de datos y su impacto en el modelo de predicción. En el trabajo se crean dos modelos de aprendizaje automático: Uno enfocado al diagnóstico del cáncer de mama utilizando indicadores de imagen, y otro focalizado en la clasificación de subtipos y descubrimiento de patrones utilizando datos genómicos y proteómicos. Las dos bases de datos elegidas son particularmente adecuadas para mostrar el funcionamiento de las técnicas de Machine Learning analizadas y del correspondiente pre-procesamiento requerido.Galarza Hernández, J. (2017). Reducción de dimensionalidad en Machine Learning. Diagnóstico de cáncer de mama bsado en datos genómicos y de imagen. http://hdl.handle.net/10251/92565TFG