Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

THE ROLE OF IMAGING BIOMARKERS DERIVED FROM PET/CT STUDIES IN DIAGNOSIS, THERAPY AND PROGNOSIS OF CANCER PATIENTS

Imaging biomarkers are features derived from one or more medical images; when validated, they can be used in the diagnosis, staging, prognosis and evaluation of treatment response of cancer patients. All imaging modalities including PET/CT, CT and MRI can allow the identification and quantitative evaluation of imaging biomarkers. The aim of this thesis was to analyze PET/CT studies performed with 18F-FDG or 68Ga-DOTA-TOC in different groups of cancer patients in order to derive imaging biomarkers and to test their role in the diagnosis, evaluation of treatment response and prognosis of various types of malignancies. The thesis will provide an overview of the studies conducted in each group of patients with non-small cell lung cancer, multiple myeloma and lymphoma, thymic epithelial tumors and neuroendocrine tumors during my PhD program

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved

질병 바이오마커 발굴 및 그와 결합하는 후각 수용체 탐색

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 화학생물공학부, 2020. 8. 박태현.Due to the development of medical technology and systems, the premature mortality rate due to disease has decreased significantly compared to the past. However, lethality from some incurable diseases including cancer is still high. Because it is difficult to feel conscious symptoms before the disease develops to a late stage, and the existing diagnosis method is inaccessible due to the invasive method and cost of examination. Due to this reason, the latest disease diagnosis technology is developing in the direction of improving accessibility, and in particular, the need for non-invasive and economic method is emerging. As a typical example, the technology for diagnosing a disease by detecting a specific volatile organic compounds enables simple diagnosis without pain because it can detect the signal of disease from exhaled breath, sweat, urine, and saliva as well as blood and body fluids. In particular, the bioelectronic sensor has demonstrated excellent selectivity and sensitivity by combining a primary transducer such as an olfactory receptor with a secondary transducer containing a nanostructured semiconductor such as carbon nanotubes or graphene. The purposes of this research are identification of disease biomarkers and screening, performance evaluation of olfactory receptors for the detection of biomarkers that are essential for development of bioeletronic sensor. The selected diseases for study are lung cancer, tuberculosis, and gastric cancer. First, the discovery of biomarkers for lung cancer and the screening of human olfactory receptors were performed. The lung cancer cell line and the normal lung cell line were cultured to compare the composition of headspace gas by GC / MS, and volatile organic compound 2-ethyl-1-hexanol, which is more frequently generated in lung cancer cell lines, was identified. In addition, human olfactory receptors capable of detecting this biomarker were screened using a dual-glo luciferase reporter gene assay. It was confirmed that the identified olfactory receptor sensitively and selectively detects the lung cancer biomarker, and then conducted olfactory nanovesicle generation and performance evaluation for use as a primary transducer of the bioelectronic sensor in the further study. In the second study, the screening of human olfactory receptors were carried out for identification of olfactory receptor capable of detecting 5 tuberculosis biomarkers found in urine [95]. The screening was conducted by transfectng the human olfactory receptor genes and the luciferase reporter gene into the HEK293 cell line to confirm the responsivity to the tuberculosis biomarkers. As a result, olfactory receptors recognizing each tuberculosis biomarker were selected, and their responsivity and selectivity were also analyzed. Third, a number of exhaled breath samples of gastric cancer patients and healthy subjects were collected and analyzed using GC/MS. As a result, butyl acid and propionic acid, which are volatile organic compounds found in relatively large amounts in the exhaled breath of gastric cancer patients, were identified. In particular, solid-phase microextraction (SPME) fibers were used as a instruments of collecting and concentrating volatile organic compounds to completely analyze the biomarkers containing a very small amount in the exhaled breath samples. To improve the reliability of the selected volatile organic compounds as biomarkers, we build a diagnostic model that distinguishes patients based on the amount of biomarkers in the exhaled breath through statistical analysis of overall data, and their sensitivity and selectivity were calculated. In addition, in order to identify a primary transducer of a bioelectronic sensor that detects biomarkers included in exhaled breath, the responsivity and selectivity of 2 human olfactory receptors known to detect butyric acid and propionic acid were estimated. Development of disease diagnosis technology is an inevitable process for universal welfare and extension of life expectancy. Diagnostic methods targeting disease-specific volatile organic compounds are attracting attention in academia as a next-generation diagnostic technology, and are actively being studied all over the world. In this thesis, several disease-specific volatile organic compounds have been newly identified, and the human olfactory receptors capable of recognizing disease biomarkers were screened. The above research results are expected to be useful for the development of sensitive and selective bioelectronic sensor for disease diagnosis.의료기술과 체계의 발달로 인해 질병으로 인한 조기 사망률은 과거에 비해 크게 줄어들었다. 그러나 암을 비롯한 일부 난치성 질병으로 인한 치사율은 여전히 높은 편이다, 이는 질병이 치명적인 수준까지 발달하기 전에 자각증상을 느끼기 힘들다는 점과 기존의 검진 방법이 특유의 침습적인 방식과 검사 비용 때문에 접근성이 떨어진다는 점에서 비롯된다. 이런 연유로 최신 질병 진단 기술은 접근성의 향상을 추구하는 방향으로 발전하고 있으며, 특히 비 침습적이고 경제적인 방법의 필요성이 대두되고 있다. 대표적인 예시로, 특이적인 휘발성 유기물질을 감지하여 질병을 진단하는 기술은 피나 체액 뿐만 아니라 날숨, 땀, 소변, 침 등을 매개로 와병 여부를 파악할 수 있기에 고통이 수반되지 않는 간단한 진단을 가능케 한다. 특히, 바이오 전자 센서는 카본나노튜브나 그라핀 같은 나노 구조 반도체를 포함한 2차 변환기에 후각 수용체와 같은 1차 변환기를 결합하여 우수한 선택도와 민감도를 선보인 바 있다. 본 연구의 목적은 질병 진단용 바이오 전자 센서 제작을 위해 필수적으로 이루어져야 하는 질병 표지물질 선정과, 표지물질 탐지를 위한 후각 수용체 발굴 및 성능 평가이다. 연구 대상으로 선택한 질병은 폐암, 결핵, 그리고 위암이다. 먼저 폐암의 표지물질 발굴과 인간 후각 수용체 탐색이 수행되었다. 폐암 세포주와 정상 폐 세포주를 배양하여 두부공간의 가스 조성을 GC/MS로 비교하였고, 폐암 세포에서 더 많이 발생하는 휘발성 유기물질 2-에틸헥산올을 특정하였다. 그리고 이 물질을 감지할 수 있는 인간 후각 수용체를 이중발광 루시퍼레이즈 검정법을 이용하여 탐색하였다. 발굴된 후각 수용체가 폐암 표지물질을 민감하고 선택적으로 감지하는 것을 확인하였으며, 향후 바이오 전자 센서의 1차 소자로 사용하기 위한 후각 나노베시클 생산 및 성능 평가를 진행하였다. 두 번째 연구에서는 소변에서 발견된 결핵 관련 5종의 휘발성 유기물질들을 감지할 수 있는 인간 후각 수용체를 탐색하였다. 탐색 과정은 HEK293 세포주에 인간 후각 수용체 유전자와 루시퍼레이즈 리포터 유전자를 형질도입하여 결핵 바이오마커들에 대한 반응성을 확인함으로써 진행되었다. 실험 결과 각각의 결핵 바이오마커에 대한 후각 수용체가 선정되었으며, 그 반응성과 선택도 또한 분석되었다. 세번째로, 위암 환자와 건강한 사람의 날숨 샘플을 다수 채취하여 GC/MS 장비를 이용해 분석하고 비교하였다. 그 결과 위암 환자에게서 상대적으로 많이 발견되는 휘발성 유기물질인 뷰틸산과 프로피온산을 특정하였다. 특히, 날숨 샘플 내에 매우 적은 양이 포함된 표지물질을 빠짐없이 분석하기 위해 휘발성 유기물질 채취 및 농축 수단으로 고체 미세추출 (SPME) 섬유를 활용하였다. 선정한 휘발성 유기물질의 표지물질로서의 신뢰도를 제고하기 위해, 전체 자료의 통계 분석 과정을 통해 날숨 내의 표지물질 포함량을 기준으로 환자 여부를 구분짓는 진단 모델을 구축하고 그 민감도와 선택도를 산출하였다. 추가적으로, 향후 진행할 날숨을 대상으로 한 위암 진단용 바이오 전자 센서 제작을 위해, 뷰틸산과 프로피온산을 감지한다고 알려진 인간 후각 수용체 2종의 반응성과 선택도를 분석하였다. 질병 진단기술 개발은 인류의 보편적 복지와 평균수명 연장을 위하여 필연적으로 이루어져야 하는 과정이다. 질병 특이적 휘발성 유기물질을 대상으로 삼는 진단 방식은 차세대 진단기술로써 학계에서 주목받고 있으며, 세계 각지에서 활발하게 연구되고 있다. 본 논문에서는 몇 가지 질병 특이적 휘발성 유기물질이 신규 발굴되었으며, 또한 기존에 알려진 질병 표지물질을 감지하는 능력을 가진 후각 수용체를 탐색하고 그 기능성을 확인하였다. 상술한 연구 성과들이 민감하고 선택적인 질병 진단용 생체 소자 개발에 유용하게 활용되길 기대한다.Chapter 1. Research Background and Objectives 1 Chapter 2. Literature Review 4 2.1 Volatolomics 5 2.2 Biomarkers of disease 6 2.2.1 Volatile organic compounds related to disease 6 2.2.2 Sources and biochemical pathways of disease-related volatile organic compounds 7 2.3 Deorphanization and application of olfactory receptors 9 Chapter 3. Experimental Procedures 11 3.1 Collection and analysis of headspace gas from cell lines 12 3.1.1 Cell culture and headspace gas sampling 12 3.1.2 Headspace gas analysis with GC/MS 15 3.2 Identification of gastric cancer biomarkers from breath 17 3.2.1 Study groups and collection of clinical data 17 3.2.2 Sampling of exhaled breath and environmental gas 19 3.2.3 SPME-GC/MS analysis 19 3.2.4 Statistical analysis 20 3.3 Gene cloning 21 3.4 Production of olfactory receptor proteins 22 3.4.1 Expression of olfactory receptors in mammalian cells 22 3.4.2 Generation of olfactory nanovesicles 23 3.5 Characterization of olfactory receptor proteins 25 3.5.1 Immunocytochemistry 25 3.5.2 Western blot analysis 25 3.5.3 Calcium signaling assay 25 3.5.4 Dual-glo luciferase assay 28 Chapter 4. Identification of lung cancer biomarkers using a cancer cell line and screening of olfactory receptors for the biomarker detection 29 4.1 Introduction 30 4.2 Collection and analysis of headspace gas of lung cancer cell line 32 4.3 Screening of human olfactory receptors recognizing 2-ethyl-1-hexanol 37 4.4 Generation and characterization of olfactory nanovesicles 39 4.5 Conclusions 41 Chapter 5. Screening of human olfactory receptors to detect tuberculosis-specific volatile organic compounds in urine 42 5.1 Introduction 43 5.2 Screening of human olfactory receptors 45 5.3 Characterization of olfactory receptors recognizing biomarkers of tuberculosis 51 5.5 Conclusions 54 Chapter 6 Identification and validation of gastric cancer biomarkers and assessment of human olfactory receptors for the biomarker detection 55 6.1 Introduction 56 6.2 Selection of SPME fiber type 58 6.3 Sampling and Analysis of Exhaled Breath 60 6.4 Changes in the amounts of VOCs in the breath of gastric cancer patients before and after surgery 65 6.5 Statistical analysis for construction of diagnostic model 70 6.6 Cell-based assay for characterization of human olfactory receptors recognizing gastric cancer biomarkers 74 6.7 Conclusions 77 Chapter 7. Overall Discussion and further suggestions 78 References 84 Appendix 1. Comparative evaluation of sensitivity to hexanal between human and canine olfactory receptors 102 A1.1 Abstract 103 A1.2 Introduction 103 A1.3 Cloning of hOR2W1 and cfOR0312 genes 105 A1.4 Expression of human and canine olfactory receptors on HEK293 cell surface 107 A1.5 Comparison of human and canine OR sensitivity to hexanal 109 A1.6 Conclusions 113 References 114 Abstract 118Docto

Neutrophil extracellular traps in cancer and cancer-associated thrombosis

Cancer is associated with a hypercoagulable state, and venous thromboembolism (VTE) may be the first sign of occult cancer. Cancer screening of all patients presenting with VTE would, however, overload the healthcare system and burden patients with unnecessary investigations. Current data suggest that neutrophil extracellular traps (NETs), prothrombotic nuclear content released by neutrophils upon strong stimulation, are central in cancer biology. This thesis aimed at a clinical investigation of the role of coagulation in advanced cancer and the role of NETs in cancer-associated thrombosis. In Study I, we evaluated the recently developed RIETE risk score to identify patients presenting with VTE and a simultaneous high risk of occult cancer. The risk score failed to identify VTE patients with a high risk of occult cancer, illustrating the need for the development of risk score models in this population. In Study II, we developed an enzyme-linked immunosorbent assay for the quantification of nucleosomal citrullinated histone H3 (H3Cit-DNA), a protein-DNA complex generated during NET formation. The assay was rigorously validated revealing high accuracy. All assay components are furthermore commercially available, enabling rapid dissemination and implementation of the assay within the field of NETs research. Study III was an exploratory study investigating several biomarkers reflecting neutrophil activation, NET formation, coagulation, and fibrinolysis and their association with mortality in 106 terminal cancer patients. Markers of neutrophil activation and NETs were associated with mortality in univariate and multivariate Cox regression. Several prior studies have revealed that markers of coagulation and fibrinolysis are associated with prognosis in cancer patients. However, no studies have investigated terminal cancer patients, and to our surprise, we did not find an association between poor prognosis and markers of coagulation and fibrinolysis. Study IV was a prospective cohort study of 500 patients presenting with acute VTE. Venous blood was sampled at the time of VTE, and markers of NETs and neutrophil activation were analyzed. H3Cit-DNA and cell-free DNA were associated with cancer diagnosis during a one-year follow-up in univariate analyses, but only H3Cit-DNA remained significant after adjustments in multivariate analyses, which could indicate a role of NETs in the development of cancer-associated thrombosis. In summary, there are as of date no accurate risk scores identifying VTE patients with underlying cancer. Through the development of an assay quantifying the NET marker H3Cit- DNA in human plasma, we found that H3Cit-DNA is elevated in advanced cancer and in patients presenting with VTE and an underlying cancer, contributing to the growing evidence of the role of NETs in cancer and cancer-associated thrombosis. Further research will determine the diagnostic potential of NETs