Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer
Yuming JiangH. WangJia WuChuanxiang ChenQiang YuanWeibin HuangTing LiShuhua XiYue HuZhiwei ZhouY. XuG. LiRuijiang Li
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Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response.Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer.Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications.In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations.In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.
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Background Although most people with relapsing onset multiple sclerosis (R-MS) eventually transition to secondary progressive multiple sclerosis (SPMS), little is known about disability progression in SPMS. Methods All R-MS patients in the Cardiff MS registry were included. Cox proportional hazards regression was used to examine a) hazard of converting to SPMS and b) hazard of attaining EDSS 6.0 and 8.0 in SPMS. Results 1611 R-MS patients were included. Older age at MS onset (hazard ratio [HR] 1.02, 95%CI 1.01–1.03), male sex (HR 1.71, 95%CI 1.41–2.08), and residual disability after onset (HR 1.38, 95%CI 1.11–1.71) were asso- ciated with increased hazard of SPMS. Male sex (EDSS 6.0 HR 1.41 [1.04–1.90], EDSS 8.0 HR 1.75 [1.14–2.69]) and higher EDSS at SPMS onset (EDSS 6.0 HR 1.31 [1.17–1.46]; EDSS 8.0 HR 1.38 [1.19–1.61]) were associated with increased hazard of reaching disability milestones, while older age at SPMS was associated with a lower hazard of progression (EDSS 6.0 HR 0.94 [0.92–0.96]; EDSS 8.0: HR 0.92 [0.90–0.95]). Conclusions Different factors are associated with hazard of SPMS compared to hazard of disability progres- sion after SPMS onset. These data may be used to plan services, and provide a baseline for comparison for future interventional studies and has relevance for new treatments for SPMS RobertsonNP@cardiff.ac.uk
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Abstract: Lung cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics, a concept introduced in 2012, refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features (watch the animation: https://youtu.be/Tq980GEVP0Y and the website www.radiomics.org). Here, we review the literature related to radiomics for lung cancer. We found 11 papers related to computed tomography (CT) radiomics, 3 to radiomics or texture analysis with positron emission tomography (PET) and 8 relating to PET/CT radiomics. There are two main applications of radiomics, the classification of lung nodules (diagnostic) or prognostication of established lung cancer (theragnostic). There are quite a few methodological issues in most of the reviewed papers. Only 5 studies, out of the 22, were externally validated. Overall, it is clear that radiomics offers great potential in improving diagnosis and patient stratification in lung cancer. It may also have a real clinical impact, as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision support in lung cancer treatment at low cost.
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The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.风险比（hazard ratio，HR）和中位生存时间是生存分析时的常规分析和报告指标。本文简要介绍了HR和中位生存时间的关系以及比例风险假定在这两者之间的作用，分析了检索出的58篇文献中的110对风险比和中位生存时间比的差异，并通过实例阐明了产生这种差异的原因。结果表明，在不满足比例风险假定时，Cox回归模型计算得到的风险比是不合理的，且与中位生存时间之比不等价。因此，在使用Cox回归模型前，应先进行比例风险假定的检验，只有符合比例风险假定时才能使用该模型；当不符合比例风险假定时，建议使用限制性平均生存时间。.
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Since the concept of radiomics was proposed, both domestic and foreign scholars have successively carried out many scientific researches on radiomics. Domestic and foreign research teams and their corresponding research have achieved certain results in radiomics, but we still face many challenges on the clinical application of radiomics-based models. Currently radiomics is still facing challenges. We should certainly pay attention to the research hotspots, existing problems, and future perspectives of radiomics.自影像组学的概念提出以来，国内外学者相继开展了众多关于影像组学的科学研究。国内外研究团队及其相应的研究在影像学上都取得了一定的成果，然而距离影像组学模型的临床应用，仍面临着诸多挑战。关于影像组学的研究热点、存在问题，以及未来的发展方向都需要关注和重视。.
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Abstract Radiomics has the potential to personalize patient treatment by using medical images that are already being acquired in clinical practice. Recently, with the development of computational and imaging technology, radiotherapy has brought unlimited opportunities driven by radiomics in individual cancer treatment and precision medicine care. This article reviews the advances in the application of radiomics in lung cancer, head and neck cancer, and other cancer sites. Additionally, we comment on the future challenges of radiomic research.
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Purpose or ObjectiveFirst dose-painting clinical trials are ongoing, even though the largest challenge of dose-painting has not been solved yet: to robustly redistribute the dose to the different regions of the tumor.Efforts to derive dose-response relations for different tumor regions rely on strong assumptions.Without accounting for uncertainty in the assumed dose-response relations, the potential gain of dose-painting may be lost.The goal of this study is to implement an automated treatment planning approach for dose-painting that takes into account uncertainties both in dose-response relations and in patient positioning directly into the optimization.Such that even in the presence of large uncertainties the delivered dosepainting plan is unlikely to perform worse than current clinical practice with homogeneous prescriptions. Material and MethodsDose response relations in TCP (tumor control probability) are modeled by a sigmoid shaped function, using 2 parameters to describe the dose level and cell sensitivity.Each voxel has its own tuple of parameters, and the parameters were assumed to follow probability distributions for which the mean and the variance were known.The expected TCP over all uncertainty distributions was optimized.Random positioning uncertainties were dealt with by convolving the pencil beam kernels with a Gaussian.For systematic geometrical uncertainties, a worst case optimization was implemented, to ensure adequate dose delivery in 95% of the geometrical scenarios.The method was implemented in our in house developed TPS and applied to a 3D ellipsoid phantom with a spherical tumor with a resistant shell and sensitive core and to a NSCLC cancer patient case with 3 subvolumes that were assumed to vary in radio-sensitivity.The effect of different probability distributions for cell sensitivity was investigated. ResultsAs expected, in the absence of dose-response and positioning uncertainties (red line), the dose to the resistant ring of the phantom (light gray in Fig 1) is considerably higher than to the sensitive core (dark gray).However, as the uncertainty in dose response relations increases (blue and green lines), the dose difference between the subvolumes decreases, even though the expected cell sensitivities do not change.Including positioning uncertainties leads to further smearing out of the dose (black line).Fig 2 demonstrates the effect on a real lung patient case with high risk GTV (white), low risk GTV (black), lymph nodes (pink).
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