Prediction of Unknown Primary Carcinoma in Head and Neck Cancer using Radiomics

The unknown primary carcinoma in head and neck cancer (HNC) is a rare disease in which cancer cells spread to lymph nodes in the upper neck, but the place where it began is unknown. The diagnostic protocol to identify the primary tumour location is challenging and invasive. In return, radiomics, a quick, low-cost, non-invasive and repeatable method, has been demonstrated in this dissertation to be a valuable tool for diagnosing the primary tumour location in these patients. The dataset analysed comprises 400 HNC patients with unknown primary carcinoma from the National Cancer Institution of Milano. The primary tumour sites already diag- nosed were Hypopharynx and Larynx (HL; n = 38), Oral Cavity (OC; n = 63), Oropharynx (OPh; n = 162) and Nasopharynx (NPh; n = 137). In total, 265 radiomic features (includ- ing shape and size, first-order, second-order, and wavelet features) were extracted from the cervical lymph nodes segmented in MRI images. The clinical information included sex, age and HPV status. Three workflows based on radiomics and machine learning methods were developed in this project. In radiomic features analysis, three correlation thresholds (0.75, 0.80, 0.85) to remove the highly correlated features and five distinctive feature selection meth- ods were assessed. The best results were achieved by the third workflow when clinical information was included in the feature set selected by Sequential Backward Selection and trained with a Linear Support Vector Machine classifier. The highest accuracies ob- tained in predicting each tumour location were 78.8% for HL, 75.4% for OC, 71.5% for OPh and 95.2% for NPh. The percentage of unclassified patients was 0.5%. The outcomes indicate that radiomics with machine learning techniques and clinical information hold the potential to predict the primary tumour site accurately.O carcinoma de tumor primário desconhecido no cancro da cabeça e do pescoço (CCP) é uma doença rara em que as células cancerígenas se espalham para os gânglios linfáticos do pescoço, mas o local onde o tumor se inicia é desconhecido. O protocolo padrão para diagnosticar o tumor primário é desafiador e invasivo. Em contrapartida, a radiómica, sendo um método rápido, de baixo custo e não invasivo, demonstrou-se neste projeto ser uma ferramenta valiosa para a localização do tumor primário nesses pacientes. O conjunto de dados analisado inclui 400 pacientes do CCP com carcinoma primá- rio desconhecido do Instituto Nacional do Cancro de Milão. Os tumores primários, já diagnosticados, foram Hipofaringe e Laringe (HL; n = 38), Cavidade Oral (CO; n = 63), Orofaringe (Oro; n = 162) e Nasofaringe (Naso; n = 137). No total, 265 características radiómicas (incluindo a forma e tamanho, características de primeira ordem, segunda ordem e características wavelets) foram extraídas dos gânglios linfáticos cervicais segmen- tados em imagens de ressonância magnética. As informações clínicas incluíam sexo, idade e a presença do vírus do papiloma humano. Três fluxos de trabalho baseados na radiómica e métodos de aprendizagem automá- tica foram desenvolvidos. Na análise de características radiómicas, foram avaliados três limiares de correlação (0, 75, 0, 80, 0, 85) para remover as características altamente corre- lacionadas e cinco métodos de seleção de características. Os melhores resultados foram alcançados pelo terceiro fluxo de trabalho quando as variáveis clínicas foram incluídas no modelo treinado (Máquina de Vetores de Suporte Linear). A precisão obtida na predição do tumor HL foi de 78, 8%, na da CO foi de 75, 4%, na do Oro foi de 71, 5% e na predição do tumor Naso foi de 95, 2%. A percentagem de pacientes não classificados foi de 0, 5%. Os resultados indicam que a radiómica em conjunto com métodos de aprendizagem automática e informações clínicas têm potencial para prever com precisão o local do tumor primário em pacientes com carcinoma de tumor primário oculto no CCP

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine