Cancer Outcome Prediction with Multiform Medical Data using Deep Learning

This thesis illustrated the work done for my PhD project, which aims to develop personalised cancer outcome prediction models using various types of medical data. A predictive modelling workflow that can analyse data with different forms and generate comprehensive outcome prediction was designed and implemented on a variety of datasets. The model development was accompanied by applying deep learning techniques for multivariate survival analysis, medical image analysis and sequential data processing. The modelling workflow was applied to three different tasks: 1. Deep learning models were developed for estimating the progression probability of patients with colorectal cancer after resection and after different lines of chemotherapy, which got significantly better predictive performance than the Cox regression models. Besides, CT-based models were developed for predicting the tumour local response after chemotherapy of patients with lung metastasis, which got an AUC of 0. 769 on disease progression detection and 0.794 on treatment response classification. 2. Deep learning models were developed for predicting the survival state of patients with non-small cell lung cancer after radiotherapy using CT scans, dose distribution and disease and treatment variables. The eventual model obtained by ensemble voting got an AUC of 0.678, which is significantly higher than the score achieved by the radiomics model (0.633). 3. Deep-learning-aided approaches were used for estimating the progression risk for patients with solitary fibrous tumours using digital pathology slides. The deep learning architecture was able to optimise the WHO risk assessment model using automatically identified levels of mitotic activity. Compared to manual counting given by pathologists, deep-learning-aided mitosis counting can re-grade the patients whose risks were underestimated. The applications proved that the predictive models based on hybrid neural networks were able to analyse multiform medical data for generating data-based cancer outcome prediction. The results can be used for realising personalised treatment planning, evaluating treatment quality, and aiding clinical decision-making

Inference for Constrained Estimation of Tumor Size Distributions

In order to develop better treatment and screening programs for cancer prevention programs, it is important to be able to understand the natural history of the disease and what factors affect its progression. We focus on a particular framework first outlined by Kimmel and Flehinger (1991, Biometrics , 47, 987–1004) and in particular one of their limiting scenarios for analysis. Using an equivalence with a binary regression model, we characterize the nonparametric maximum likelihood estimation procedure for estimation of the tumor size distribution function and give associated asymptotic results. Extensions to semiparametric models and missing data are also described. Application to data from two cancer studies is used to illustrate the finite-sample behavior of the procedure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65536/1/j.1541-0420.2008.01001.x.pd

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

Bayesian Approach For Early Stage Event Prediction In Survival Data

Predicting event occurrence at an early stage in longitudinal studies is an important and challenging problem which has high practical value. As opposed to the standard classification and regression problems where a domain expert can provide the labels for the data in a reasonably short period of time, training data in such longitudinal studies must be obtained only by waiting for the occurrence of sufficient number of events. On the other hand, survival analysis aims at finding the underlying distribution for data that measure the length of time until the occurrence of an event. However, it cannot give an answer to the open question of how to forecast whether a subject will experience event by end of study having event occurrence information at early stage of survival data?\u27\u27. This problem exhibits two major challenges: 1) absence of complete information about event occurrence (censoring) and 2) availability of only a partial set of events that occurred during the initial phase of the study. Thus, the main objective of this work is to predict for which subject in the study event will occur at future based on few event information at the initial stages of a longitudinal study. In this thesis, we propose a novel approach to address the first challenge by introducing a new method for handling censored data using Kaplan-Meier estimator. The second challenge is tackled by effectively integrating Bayesian methods with an Accelerated Failure Time (AFT) model by adapting the prior probability of the event occurrence for future time points. In another word, we propose a novel Early Stage Prediction (ESP) framework for building event prediction models which are trained at early stages of longitudinal studies. More specifically, we extended the Naive Bayes, Tree-Augmented Naive Bayes (TAN) and Bayesian Network methods based on the proposed framework, and developed three algorithms, namely, ESP-NB, ESP-TAN and ESP-BN, to effectively predict event occurrence using the training data obtained at early stage of the study. The proposed framework is evaluated using a wide range of synthetic and real-world benchmark datasets. Our extensive set of experiments show that the proposed ESP framework is able to more accurately predict future event occurrences using only a limited amount of training data compared to the other alternative prediction methods