Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models
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In this study, we aim to evaluate the diagnostic performance of diffusion tensor imaging (DTI) in detecting prostatic peripheral zone cancer and to deduce its clinical utility. The area under curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic efficiency of ADC value, FA value and their combination. The diagnostic threshold values of ADC and FA were also determined. It was concluded that DTI quantitative indexes has moderately high diagnostic accuracy in detecting prostate cancer , while the ADC value had higher diagnostic efficiency than the FA value.
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Objective To explore the diagnostic value of contrast-enhanced sonography for breast cancer by receiver operating characteristic curve (ROC curve) and a model of logistic regression. Methods Contrast-enhanced sonography was performed preoperatively in 92 women with breast mass. After analyzing the sonographic findings with logistic stepwise regression, we screened multiple diagnostic parameters for breast cancer and established a mathematical model for diagnosis. Then we assessed the diagnostic efficacy of the model and calculated the diagnostic cut-off points for breast cancer using the ROC curve. Results The area under ROC curve (AUC) of the rising slope combined with the morphological characteristics of blood flow was greater than that of either parameter alone (P 0.05). According to the regression equation P = 1 / [1+e-(-3.637+0.856X+3.153A1+3.572A2)], the cut-off point, sensitivity, specificity, and accuracy of the combined parameters for diagnosing breast cancer were 0.659, 95.8%, 84.2%, and 91.3%, respectively. Conclusion The model of logistic regression is helpful to improve the diagnostic efficacy of contrast-enhanced sonography for breast cancer.
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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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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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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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