Kidney Cancer has numerous subtypes with Clear Cell Renal Cell Carcinoma (ccRCC) being the most common. Pre-existing prognostic models have not been validated in Canadian patients for recurrence free survival (RFS) and other outcomes. We conducted four studies: 1) externally validated pre-existing RCC prognostic models; 2) assessed the impact of baseline hazard function miscalibration on model assessment; 3) created new models and risk groups for RFS in non-metastatic ccRCC patients; 4) compared new risk groups to existing Canadian guidelines and created new imaging schedules. Pre-existing model performance varied considerably with some models performing well. The effect of baseline hazard function miscalibration varied across distribution shapes but the calibration slope was useful in relatively ranking prognostic model performance. The CKCis prognostic model and risk groups performed better than the existing CUA risk groups. Based on CKCis risk groups fewer scans are recommended in low-risk patients and more scans are recommended in higher risk patients. External validation of the CKCis model is required to assess clinical utility in different populations.

Estudos de prognóstico clínico consistem na predição do curso de uma doença em pacientes e são utilizados por profissionais da saúde com o intuito de aumentar as chances ou a qualidade de sua recuperação. Sob a perspectiva computacional, a criação de um modelo prognóstico clínico é um problema de classificação, cujo objetivo é identificar a qual classe (dentro de um conjunto de classes predefinidas) uma nova amostra pertence. Este projeto visa a criar modelos prognósticos de lesões traumáticas do plexo braquial, um conjunto de nervos que inervam os membros superiores, utilizando dados de pacientes adultos com esse tipo de lesão. Os dados são provenientes do Instituto de Neurologia Deolindo Couto (INDC) da Universidade Federal do Rio de Janeiro (UFRJ) e contêm dezenas de atributos clínicos coletados por meio de questionários eletrônicos. Com esses modelos prognósticos, deseja-se identificar de maneira automática os possíveis preditores do curso desse tipo de lesão. Árvores de decisão são classificadores frequentemente utilizados para criação de modelos prognósticos, por se tratarem de um modelo transparente, cujo resultado pode ser examinado e interpretado clinicamente. As Florestas Aleatórias, uma técnica que utiliza um conjunto de árvores de decisão para determinar o resultado final da classificação, podem aumentar significativamente a acurácia e a generalização dos modelos gerados, entretanto ainda são pouco utilizadas na criação de modelos prognósticos. Neste projeto, exploramos a utilização de florestas aleatórias nesse contexto, bem como a aplicação de métodos de interpretação de seus modelos gerados, uma vez que a transparência do modelo é um aspecto particularmente importante em domínios clínicos. A estimativa de generalização dos modelos resultantes foi feita por meio de métodos que viabilizam sua utilização sobre um número reduzido de instâncias, uma vez que os dados relativos ao prognóstico são provenientes de 44 pacientes do INDC. Além disso, adaptamos a técnica de florestas aleatórias para incluir a possível existência de valores faltantes, que é uma característica presente nos dados utilizados neste projeto. Foram criados quatro modelos prognósticos - um para cada objetivo de recuperação, sendo eles a ausência de dor e forças satisfatórias avaliadas sobre abdução do ombro, flexão do cotovelo e rotação externa no ombro. As acurácias dos modelos foram estimadas entre 77% e 88%, utilizando o método de validação cruzada leave-one-out. Esses modelos evoluirão com a inclusão de novos dados, provenientes da contínua chegada de novos pacientes em tratamento no INDC, e serão utilizados como parte de um sistema de apoio à decisão clínica, de forma a possibilitar a predição de recuperação de um paciente considerando suas características clínicas. / Studies of prognosis refer to the prediction of the course of a disease in patients and are employed by health professionals in order to improve patients\' recovery chances and quality. Under a computational perspective, the creation of a prognostic model is a classification task that aims to identify to which class (within a predefined set of classes) a new sample belongs. The goal of this project is the creation of prognostic models for traumatic injuries of the brachial plexus, a network of nerves that innervates the upper limbs, using data from adult patients with this kind of injury. The data come from the Neurology Institute Deolindo Couto (INDC) of Rio de Janeiro Federal University (UFRJ) and they are characterized by dozens of clinical features that are collected by means of electronic questionnaires. With the use of these prognostic models we intended to automatically identify possible predictors of the course of brachial plexus injuries. Decision trees are classifiers that are frequently used for the creation of prognostic models since they are a transparent technique that produces results that can be clinically examined and interpreted. Random Forests are a technique that uses a set of decision trees to determine the final classification results and can significantly improve model\'s accuracy and generalization, yet they are still not commonly used for the creation of prognostic models. In this project we explored the use of random forests for that purpose, as well as the use of interpretation methods for the resulting models, since model transparency is an important aspect in clinical domains. Model assessment was achieved by means of methods whose application over a small set of samples is suitable, since the available prognostic data refer to only 44 patients from INDC. Additionally, we adapted the random forests technique to include missing data, that are frequent among the data used in this project. Four prognostic models were created - one for each recovery goal, those being absence of pain and satisfactory strength evaluated over shoulder abduction, elbow flexion and external shoulder rotation. The models\' accuracies were estimated between 77% and 88%, calculated through the leave-one-out cross validation method. These models will evolve with the inclusion of new data from new patients that will arrive at the INDC and they will be used as part of a clinical decision support system, with the purpose of prediction of a patient\'s recovery considering his or her clinical characteristics.

Estudos de prognóstico clínico consistem na predição do curso de uma doença em pacientes e são utilizados por profissionais da saúde com o intuito de aumentar as chances ou a qualidade de sua recuperação. Sob a perspectiva computacional, a criação de um modelo prognóstico clínico é um problema de classificação, cujo objetivo é identificar a qual classe (dentro de um conjunto de classes predefinidas) uma nova amostra pertence. Este projeto visa a criar modelos prognósticos de lesões traumáticas do plexo braquial, um conjunto de nervos que inervam os membros superiores, utilizando dados de pacientes adultos com esse tipo de lesão. Os dados são provenientes do Instituto de Neurologia Deolindo Couto (INDC) da Universidade Federal do Rio de Janeiro (UFRJ) e contêm dezenas de atributos clínicos coletados por meio de questionários eletrônicos. Com esses modelos prognósticos, deseja-se identificar de maneira automática os possíveis preditores do curso desse tipo de lesão. Árvores de decisão são classificadores frequentemente utilizados para criação de modelos prognósticos, por se tratarem de um modelo transparente, cujo resultado pode ser examinado e interpretado clinicamente. As Florestas Aleatórias, uma técnica que utiliza um conjunto de árvores de decisão para determinar o resultado final da classificação, podem aumentar significativamente a acurácia e a generalização dos modelos gerados, entretanto ainda são pouco utilizadas na criação de modelos prognósticos. Neste projeto, exploramos a utilização de florestas aleatórias nesse contexto, bem como a aplicação de métodos de interpretação de seus modelos gerados, uma vez que a transparência do modelo é um aspecto particularmente importante em domínios clínicos. A estimativa de generalização dos modelos resultantes foi feita por meio de métodos que viabilizam sua utilização sobre um número reduzido de instâncias, uma vez que os dados relativos ao prognóstico são provenientes de 44 pacientes do INDC. Além disso, adaptamos a técnica de florestas aleatórias para incluir a possível existência de valores faltantes, que é uma característica presente nos dados utilizados neste projeto. Foram criados quatro modelos prognósticos - um para cada objetivo de recuperação, sendo eles a ausência de dor e forças satisfatórias avaliadas sobre abdução do ombro, flexão do cotovelo e rotação externa no ombro. As acurácias dos modelos foram estimadas entre 77% e 88%, utilizando o método de validação cruzada leave-one-out. Esses modelos evoluirão com a inclusão de novos dados, provenientes da contínua chegada de novos pacientes em tratamento no INDC, e serão utilizados como parte de um sistema de apoio à decisão clínica, de forma a possibilitar a predição de recuperação de um paciente considerando suas características clínicas. / Studies of prognosis refer to the prediction of the course of a disease in patients and are employed by health professionals in order to improve patients\' recovery chances and quality. Under a computational perspective, the creation of a prognostic model is a classification task that aims to identify to which class (within a predefined set of classes) a new sample belongs. The goal of this project is the creation of prognostic models for traumatic injuries of the brachial plexus, a network of nerves that innervates the upper limbs, using data from adult patients with this kind of injury. The data come from the Neurology Institute Deolindo Couto (INDC) of Rio de Janeiro Federal University (UFRJ) and they are characterized by dozens of clinical features that are collected by means of electronic questionnaires. With the use of these prognostic models we intended to automatically identify possible predictors of the course of brachial plexus injuries. Decision trees are classifiers that are frequently used for the creation of prognostic models since they are a transparent technique that produces results that can be clinically examined and interpreted. Random Forests are a technique that uses a set of decision trees to determine the final classification results and can significantly improve model\'s accuracy and generalization, yet they are still not commonly used for the creation of prognostic models. In this project we explored the use of random forests for that purpose, as well as the use of interpretation methods for the resulting models, since model transparency is an important aspect in clinical domains. Model assessment was achieved by means of methods whose application over a small set of samples is suitable, since the available prognostic data refer to only 44 patients from INDC. Additionally, we adapted the random forests technique to include missing data, that are frequent among the data used in this project. Four prognostic models were created - one for each recovery goal, those being absence of pain and satisfactory strength evaluated over shoulder abduction, elbow flexion and external shoulder rotation. The models\' accuracies were estimated between 77% and 88%, calculated through the leave-one-out cross validation method. These models will evolve with the inclusion of new data from new patients that will arrive at the INDC and they will be used as part of a clinical decision support system, with the purpose of prediction of a patient\'s recovery considering his or her clinical characteristics.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19624号 / 医博第4131号 / 新制||医||1015(附属図書館) / 32660 / 京都大学大学院医学研究科医学専攻 / (主査)教授 武藤 学, 教授 今中 雄一, 教授 佐藤 俊哉 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23784号 / 医博第4830号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 中島 貴子, 教授 伊達 洋至 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Objectives: In our study, we evaluated and compared the prognostic value and performance of the 6th, 7th, and 8th editions of the American Joint Committee on Cancer (AJCC) staging system in patients undergoing surgery for perihilar cholangiocarcinoma (PHC). Methods: Patients undergoing liver surgery with curative intention for PHC between 2002 and 2019 were identified from a prospective database. Histopathological parameters and stage of the PHC were assessed according to the 6th, 7th, and 8th editions of the tumor node metastasis (TNM) classification. The prognostic accuracy between staging systems was compared using the area under the receiver operating characteristic curve (AUC) model. Results: Data for a total of 95 patients undergoing liver resection for PHC were analyzed. The median overall survival time was 21 months (95% CI 8.1–33.9), and the three- and five-year survival rates were 46.1% and 36.2%, respectively. Staging according to the 8th edition vs. the 7th edition resulted in the reclassification of 25 patients (26.3%). The log-rank p-values for the 7th and 8th editions were highly statistically significant (p ≤ 0.01) compared to the 6th edition (p = 0.035). The AJCC 8th edition staging system showed a trend to better discrimination, with an AUC of 0.69 (95% CI: 0.52–0.84) compared to 0.61 (95% CI: 0.51–0.73) for the 7th edition. Multivariate survival analysis revealed male gender, age >65 years, positive resection margins, presence of distant metastases, poorly tumor differentiation, and lymph node involvement, such as no caudate lobe resection, as independent predictors of poor survival (p < 0.05). Conclusions: In the current study, the newly released 8th edition of AJCC staging system showed no significant benefit compared to the previous 7th edition in predicting the prognosis of patients undergoing liver resection for perihilar cholangiocarcinoma. Further research may help to improve the prognostic value of the AJCC staging system for PHC—for instance, by identifying new prognostic markers or staging criteria, which may improve that individual patient’s outcome.

Le glioblastome multiforme (GBM) est une tumeur de grade IV représentant 49% de toutes les tumeurs cérébrales. Malgré des modalités de traitement agressives (radiothérapie, chimiothérapie et résection chirurgicale), le pronostic est mauvais avec une survie globale médiane de 12 à 14 mois. Les aractéristiques issues de la neuro imagerie des GBM peuvent fournir de nouvelles opportunités pour la classification, le pronostic et le développement de nouvelles thérapies ciblées pour faire progresser la pratique clinique. Cette thèse se concentre sur le développement de modèles pronostiques exploitant des caractéristiques de radiomique extraites des images multimodales IRM (T1 pré- et post-contraste, T2 et FLAIR). Le contexte méthodologique proposé consiste à i) recaler tous les volumes multimodaux IRM disponibles et en segmenter un volume tumoral unique, ii) extraire des caractéristiques radiomiques et iii) construire et valider les modèles pronostiques par l’utilisation d’algorithmes d’apprentissage automatique exploitant des cohortes cliniques multicentriques de patients. Le coeur des méthodes développées est fondé sur l’extraction de radiomiques (incluant des paramètres d’intensité, de forme et de textures) pour construire des modèles pronostiques à l’aide de deux algorithmes d’apprentissage, les machines à vecteurs de support (support vector machines, SVM) et les forêts aléatoires (random forest, RF), comparées dans leur capacité à sélectionner et combiner les caractéristiques optimales. Les bénéfices et l’impact de plusieurs étapes de pré-traitement des images IRM (re-échantillonnage spatial des voxels, normalisation, segmentation et discrétisation des intensités) pour une extraction de métriques fiables ont été évalués. De plus les caractéristiques radiomiques ont été standardisées en participant à l’initiative internationale de standardisation multicentrique des radiomiques. La précision obtenue sur le jeu de test indépendant avec les deux algorithmes d’apprentissage SVM et RF, en fonction des modalités utilisées et du nombre de caractéristiques combinées atteignait 77 à 83% en exploitant toutes les radiomiques disponibles sans prendre en compte leur fiabilité intrinsèque, et 77 à 87% en n’utilisant que les métriques identifiées comme fiables.Dans cette thèse, un contexte méthodologique a été proposé, développé et validé, qui permet la construction de modèles pronostiques dans le cadre des GBM et de l’imagerie multimodale IRM exploitée par des algorithmes d’apprentissage automatique. Les travaux futurs pourront s’intéresser à l’ajout à ces modèles des informations contextuelles et génétiques. D’un point de vue algorithmique, l’exploitation de nouvelles techniques d’apprentissage profond est aussi prometteuse. / Glioblastoma multiforme (GBM) is a WHO grade IV tumor that represents 49% of ail brain tumours. Despite aggressive treatment modalities (radiotherapy, chemotherapy and surgical resections) the prognosis is poor, as médian overall survival (OS) is 12-14 months. GBM’s neuroimaging (non-invasive) features can provide opportunities for subclassification, prognostication, and the development of targeted therapies that could advance the clinical practice. This thesis focuses on developing a prognostic model based on multimodal MRI-derived (Tl pre- and post-contrast, T2 and FLAIR) radiomics in GBM. The proposed methodological framework consists in i) registering the available 3D multimodal MR images andsegmenting the tumor volume, ii) extracting radiomics iii) building and validating a prognostic model using machine learning algorithms applied to multicentric clinical cohorts of patients. The core component of the framework rely on extracting radiomics (including intensity, shape and textural metrics) and building prognostic models using two different machine learning algorithms (Support Vector Machine (SVM) and Random Forest (RF)) that were compared by selecting, ranking and combining optimal features. The potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of the voxels, intensities quantization and normalization, segmentation) for reliable extraction of radiomics was thoroughly assessed. Moreover, the standardization of the radiomics features among methodological teams was done by contributing to “Multicentre Initiative for Standardisation of Radiomics”. The accuracy obtained on the independent test dataset using SVM and RF reached upto 83%- 77% when combining ail available features and upto 87%-77% when using only reliable features previously identified as robust, depending on number of features and modality. In this thesis, I developed a framework for developing a compréhensive prognostic model for patients with GBM from multimodal MRI-derived “radiomics and machine learning”. The future work will consists in building a unified prognostic model exploiting other contextual data such as genomics. In case of new algorithm development we look forward to develop the Ensemble models and deep learning-based techniques.