Health-related quality of life in advanced non-small cell lung cancer : a methodological appraisal based on a systematic literature review

Background: The majority of lung cancer patients are diagnosed with advanced non-small cell lung cancer (NSCLC), the bulk of which receive palliative systemic treatment with the goal to provide effective symptom palliation and safeguard health-related quality of life (HRQoL). Advanced NSCLC trials with HRQoL endpoints face methodological constraints limiting interpretability. Objectives: We provide a comprehensive overview of recent clinical trials evaluating the impact of systemic therapies on HRQoL in advanced NSCLC, focusing on the methodological quality, with the ultimate goal to improve interpretation, comparison and reporting of HRQoL data. Methods: A systematic literature review was performed. Prospective studies published over the last decade evaluating the impact of systemic treatments on HRQoL in advanced NSCLC were included. Methodological quality of HRQoL reporting was assessed with the CONSORT-PRO extension. Results: Hundred-twelve manuscripts describing 85 trials met all criteria. No formal conclusion can be drawn regarding the impact on HRQoL of different treatments. We report an important variety in methodological quality in terms of definitions of HRQoL, missing data points, lack of standardization of analyzing and presenting HRQoL and no standard follow-up time. The quality of HRQoL data reporting varies substantially between studies but improves over time. Conclusion: This review shows that in the heterogeneous landscape of trials addressing HRQoL in advanced stage NSCLC. Methodology reporting remains generally poor. Adequate reporting of HRQoL outcome data is equally important to support clinical decision-making as to correctly inform health policy regarding direct approval and reimbursement of the new drugs and combinations that will come online

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT

An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy

Application of statistical and decision-analytic models for evidence synthesis for decision-making in public health and the healthcare sector

With the awareness that healthcare is a limited resource, decision-makers are challenged to allocate it rationally and efficiently. Health economic methods of evidence synthesis for decision-making are useful to quantify healthcare resource utilisation, critically evaluate different interventions and ensure the implementation of the most effective or cost-effective strategy. The nine studies included in the present cumulative doctoral thesis aim to demonstrate the capability of statistical and decision-analytic modelling techniques to inform and support rational healthcare decision-making in Germany. Five studies apply statistical modelling in analyses of public health and health economic data. They show that the developed models are valuable instruments for examining patterns in the data and generating knowledge from observable data which can further be used in devising disease management and care programs as well as economic evaluations. Further, two health economic evaluations, which adopt the decision-analytic-modelling approach, show that decision-analytic modelling is a powerful tool to represent the epidemiology of infectious and non-infectious diseases on a population level, quantify the burden of the diseases, generalise the outcomes of clinical trials, and predict how the interventions can change the impact of the diseases on the health of the population. Additionally, two literature reviews examine the application of decision-analytic modelling in health economic evaluations. The first study reviews and empirically analyses health technology assessments by the German Institute for Medical Documentation and Information and demonstrates that the application of decision-analytic models improves the evidence produced for policy-making in the healthcare sector in Germany. The second systematic review focuses on methodological choices made in constructing decision-analytic models and explains how critically the structural and parametrical assumptions can influence the final message of the economic evaluations and shows that building a validated, reliable model as well as the transparent reporting is of high priority in facilitating the communication and implementation of the most cost-effective course of action. Overall, the present thesis shows the relevance and advantage of the application of models in synthesising evidence for decision-making. The included studies contribute to the current and future development of the methods used to address the problems of health economic efficiency. Further advances in the computational modelling techniques and data collection, from one side, will ease the decision-making process, but, from another side, will require increasing competence and understanding within the decision-making bodies

Lung Disease Classification using Dense Alex Net Framework with Contrast Normalisation and Five-Fold Geometric Transformation

lung disease is one of the leading causes of death worldwide. Most cases of lung diseases are found when the disease is in an advanced stage. Therefore, the development of systems and methods that begin to diagnose quickly and prematurely plays a vital role in today's world. Currently, in detecting differences in lung cancer, an accurate diagnosis of cancer types is needed. However, improving the accuracy and reducing training time of the diagnosis remains a challenge. In this study, we have developed an automated classification scheme for lung cancer presented in histopathological images using a dense Alex Net framework. The proposed methodology carries out several phases includes pre-processing, contrast normalization, data augmentation and classification. Initially, the pre-processing step is accompanied to diminish the noisy contents present in the image. Contrast normalization has been explored to maintain the same illumination factor among histopathological lung images next to pre-processing. Afterwards, data augmentation phase has been carried out to enhance the dataset further to avoid over-fitting problems. Finally, the Dense Alex Net is utilized for classification that comprises five convolutional layers, one multi-scale convolution layer, and three fully connected layers. In evaluation experiments, the proposed approach was trained using our original database to provide rich and meaningful features. The accuracy attained by the proposed methodology is93%, which is maximum compared with the existing algorithm

환자맞춤형 치료를 위한 체외 항암제 스크리닝용 바이오칩

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 권성훈.정밀의학(Precision Medicine) 혹은 개인맞춤의학(Personalized Medicine)은 개개인의 최적화된 치료방법을 결정하는 것을 목표로 하는 의학의 패러다임이다. 특히, 임상종양학에서는 차세대염기서열분석(NGS), 전사체서열분석, 그리고 질량분석법들을 통한 환자의 분자 프로파일(molecular profile) 방법이 발전해오고 있으며, 이를 바탕으로 환자를 세분화하여 맞춤형 치료를 구현하려고 노력해오고 있다. 하지만, 여전히 현 수준에서 이해되지 못하는 수준의 종양 이질성(tumor heterogeneity)과 오랜 처방기록을 가진 환자군들의 항암제 획득내성(acquired resistance) 등의 원인으로 맞춤형 환자 처방은 쉽지 않은 경우가 많다. 이러한 경우 환자로부터 얻어진 암세포, 조직으로부터 얻어진 일차세포 혹은 체외 배양된 세포, 스페로이드, 장기유사체 등을 이용하여 고속다중약물스크리닝기술을 통한 맞춤형 항암제를 선별해내는 체외 약물진단 기술을 생각해낼 수 있는데, 이는 기존의 유전체 기반의 시도와 병행되어 개개의 환자들에게 더욱 적합한 치료방법을 찾는 것이 가능하게 한다. 하지만 이러한 목적의 고속다중약물스크리닝기술은 높은 활용가능성에도 불구하고, 광범위한 보급과 활용이 되기에는 제약점이 많았다. 기존의 고속다중약물스크리닝기술은 많은 양의 샘플이 소모되고, 값비싼 시약의 소모량도 적지 않았다. 게다가, 수천 가지 이상의 서로 다른 물질들을 탐색하기 위해 반드시 필요한 고가의 자동화된 액체 운반기(liquid handler) 등이 필요하였는데, 이러한 문제로 대형 제약사, 연구소 등을 제외하고는 도입이 쉽지가 않아 기술접근성이 제한되어 있었다. 본 연구에서는 반도체공정에서의 노광기술을 이용하여 개개의 식별할 수 있는 코드를 가지고 있는 코드화된 하이드로젤 기반의 광경화성폴리머 미세입자를 만들어, 이를 원하는 암세포에 약물 스크리닝을 해보고자 하는 다양한 약물라이브러리를 이용 각각의 코드화된 미세입자에 흡수시켜 약물-미세입자 라이브러리를 제작한다. 그후, 값비싼 어레이 제작용 스포터 혹은 디스펜서 장비없이 간단한 자기조립을 통해 대규모의 다양한 약물-하이드로젤 어레이를 제작할 수 있는 기술을 개발하였다. 또한, 소량의 세포들 만으로도 미세우물(microwell) 기반의 세포칩에 도포하는 방식을 개발하였으며, 이를통해 약물-하이드로젤 어레이와 미세우물기반의 세포칩의 결합으로 수백-수천의 다양한 어세이를 적은 수의 샘플만으로도 한번에 수행할 수 있는 고속다중약물스크리닝 기술을 수행할 수 있게 만들었다. 본 연구에서 제시한 소형화된 체외 항암제 스크리닝용 약물플랫폼은 다음과 같은 의의를 가진다. 적은 수의 환자세포 혹은 샘플의 양에 적용할 수 있는, 사용하기 손쉬운 기술로서, 기존의 값비싼 장비, 시약의 사용량을 획기적으로 줄일 수 있는 기술이다. 본 연구에서 제안된 기술을 통해 기존의 장비를 사용할 때 시약의 값이 비싸거나, 장비의 가격이 비싸서, 혹은 다루고자 하는 샘플의 양이 제한적이어서 기존에 접근하기 힘들었던 다양한 학술연구에 적용할 수 있으며, 병원에서의 임상연구 및 실제 환자맞춤형 치료에 사용 될 수 있는 접근성을 획기적으로 높일 수 있다. 특히, 비교적 중,소 규모의 연구환경에서도 다양한 희귀한 환자유래세포 혹은 환자유래오가노이드 등과 접목하여 사용된다면 본 플랫폼의 가능성을 더욱 극대화 할 수 있을 것으로 기대한다.Precision or Personalized Medicine is a medical paradigm aimed to determine optimal therapy for individual patient. In particular, clinical oncology has been using methods of molecular profiling for each patient through next-generation sequencing (NGS), mRNA-sequencing, and mass spectrometry, and has been trying to implement personalized treatment. However, personalized treatment based on molecular profiling to each patient is not always possible due to the high level of heterogeneity of tumor that is still not fully understood at the current level and acquired resistance of anti-cancer drug due to cumulative targeted therapy. In such cases, in vitro drug testing platform using primary cells obtained from patients, or patient-derived cells, spheroids, and organoids can make it possible to find a more appropriate treatment for each individual patient. However, though high-throughput drug screening technology for this purpose is of the utmost importance in saving lives, there were many limitations to its wide use in many hospitals. The existing high-throughput drug combination screening technology consumes a large number of samples and consumes a considerable amount of expensive reagents. In addition, expensive automated liquid handlers, which were essential for exploring thousands of different pipetting, were not easy to introduce except for large-sized pharmaceutical companies and research institutes, which limited access to technology. In this study, I construct a heterogeneous drug-loaded microparticle library by fabricating encoded photocurable polymer particle that has individually identifiable codes to track loaded drug. and I load various drug molecules, which I want to test to target cells, into each coded microparticle. Then, I developed to produce heterogeneous drug-laden microparticle arrays through simple self-assembly without the need for a microarray spotter or dispensing machine for generating microarray. I also have developed cell seeding method of seeding small-volume samples into the microwell-based cell chip. By utilizing the drug-laden microparticle hydrogel array and microwell-based cell chip technology, hundreds to thousands of different assays can be done at once with just a small number of samples and low cost. Through the implemented platform, the anti-cancer drug sequential combination screening was conducted on the triple-negative breast cooler (TNBC) cells, which are generally known to be difficult to treat due to lack of known drug target, and the results of screening were analyzed by establishing a library of drugs in the EGFR inhibitory type and drugs in the genotoxin type. In addition, another study was conducted to find optimal drug combinations using patient-derived cells derived from tumors in patients with non-small cell lung cancer that have obtained acquired resistance. Finally, as the growing need for three-dimensional culture, such as spheroid and organoid for having a similar response to in vivo drug testing, it was also developed that microwell-based cell chip that is capable of 3D culture with low-cost and small-volume of cells. The miniaturized in vitro anticancer drug screening platform presented in this study has the following significance. An easy-to-use technique that can be applied to a small number of patient cells or samples, which can dramatically reduce the use of conventional expensive equipment, reagents. The proposed technology in this study can be applied to a variety of academic studies previously inaccessible to high-throughput screening due to the high cost of reagents, the high price of equipment, or the limited amount of samples in conventional drug screening. and this platform can also dramatically increase access to clinical research in hospitals for personalized treatments. In particular, it is expected that the possibility of this platform will be further maximized if it is used in a relatively small and medium-sized research environment by the combined use of various rare samples such as patient-derived cells or patient-derived organoids.Chapter 1 Introduction １ 1.1 Motivation of this research ２ 1.2 Competing technologies and Previous works ８ 1.3 Main Concept: In vitro drug testing using miniaturized encoded drug-laden hydrogel array technology １５ Chapter 2 Platform Development of Drug Releasing Hydrogel Microarray ２０ 2.1 Encoded Drug-Laden Hydrogel & Library construction ２１ 2.2 Array generation of heterogenous drug-laden microparticles. ３４ 2.3 Cell Culturing on Cell Chip and bioassay ３６ Chapter 3 Sequential Drug Combination Screening Assy on TNBC ４０ 3.1 Background : Sequential Drug Combination as promising therapeutic option ４１ 3.2 Experimental design with sequential drug treatment assay ４３ 3.3 Technical Issue & its engineering solution ４４ 3.4 Assay Result ４９ Chapter 4 Drug Combination Assay on Patient-Derived Cells ５８ 4.1 Background : Simultaneous Combination Treatment using Patient-Derived Cells ５９ 4.2 Improvement of Platform for facilitating translational study ６２ 4.3 Study Design for small-volume drug combinatorial screening with NSCLC patient derived cell ６５ 4.4 Assay Result ６９ Chapter 5 Development of platform for 3D culture model ７２ 5.1 3D culturable platform ７３ 5.2 Development of 3D culture platform based Matrigel scaffold. ７８ 5.3 Advantage over conventional 3D culture-based drug testing platform. ８５ Chapter 6 Conclusion ８７ Bibliography ９０ Abstract in Korean ９７Docto