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1. Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms

   https://doi.org/10.3389/fnins.2022.884708  

   Ghosh, Debashis ; Mastej, Emily ; Jain, Rajan ; Choi, Yoon Seong ( June 2022 , Frontiers in Neuroscience)

   The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma. 

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2. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

   https://doi.org/10.3390/cancers14071654  

   Preuss, Kiersten ; Thach, Nate ; Liang, Xiaoying ; Baine, Michael ; Chen, Justin ; Zhang, Chi ; Du, Huijing ; Yu, Hongfeng ; Lin, Chi ; Hollingsworth, Michael A. ; et al ( April 2022 , Cancers)

   As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization. 

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3. Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8 + T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

   https://doi.org/10.1002/jmri.27871  

   Bian, Yun ; Liu, Cong ; Li, Qi ; Meng, Yinghao ; Liu, Fang ; Zhang, Hao ; Fang, Xu ; Li, Jing ; Yu, Jieyu ; Feng, Xiaochen ; et al ( August 2021 , Journal of Magnetic Resonance Imaging)

   CD8⁺T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8⁺T‐cell expression can better identify the population benefitting from immunotherapy.

   To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8⁺T‐cell expression in patients with PDAC.

   Retrospective cohort study.

   Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts.

   Breath‐hold single‐shot fast‐spin echo T2‐weighted sequence and noncontrast T1‐weighted fat‐suppressed sequences.

   CD8⁺T‐cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1‐ and T2‐weighted images and reduced using the Wilcoxon rank‐sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility.

   Kaplan–Meier estimates, Student's t‐test, the Kruskal–Wallis H test, and the chi‐square test, receiver operating characteristic curve, and decision curve analysis.

   A log‐rank test showed that the survival duration in the CD8‐high group (25.51 months) was significantly longer than that in the CD8‐low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77–0.92) and 0.69 (95% CI, 0.53–0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77–0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61–0.87).

   This study developed a noncontrast MRI‐based radiomics model that can preoperatively determine CD8⁺T‐cell expression in patients with PDAC and potentially immunotherapy planning.

   5

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4. Deep segmentation networks predict survival of non-small cell lung cancer

   https://doi.org/10.1038/s41598-019-53461-2  

   Baek, Stephen ; He, Yusen ; Allen, Bryan G. ; Buatti, John M. ; Smith, Brian J. ; Tong, Ling ; Sun, Zhiyu ; Wu, Jia ; Diehn, Maximilian ; Loo, Billy W. ; et al ( December 2019 , Scientific Reports)

   Abstract Non-small-cell lung cancer (NSCLC) represents approximately 80–85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment. 

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5. Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer

   https://doi.org/10.1088/1361-6560/ac515b  

   Yang, Jing ; Wang, Li ; Qin, Jiale ; Du, Jichen ; Ding, Mingchao ; Niu, Tianye ; Li, Rencang ( February 2022 , Physics in Medicine & Biology)

   Abstract Purpose. This study aims to develop and validate a multi-view learning method by the combination of primary tumor radiomics and lymph node (LN) radiomics for the preoperative prediction of LN status in gastric cancer (GC). Methods. A total of 170 contrast-enhanced abdominal CT images from GC patients were enrolled in this retrospective study. After data preprocessing, two-step feature selection approach including Pearson correlation analysis and supervised feature selection method based on test-time budget (FSBudget) was performed to remove redundance of tumor and LN radiomics features respectively. Two types of discriminative features were then learned by an unsupervised multi-view partial least squares (UMvPLS) for a latent common space on which a logistic regression classifier is trained. Five repeated random hold-out experiments were employed. Results. On 20-dimensional latent common space, area under receiver operating characteristic curve (AUC), precision, accuracy, recall and F1-score are 0.9531 ± 0.0183, 0.9260 ± 0.0184, 0.9136 ± 0.0174, 0.9468 ± 0.0106 and 0.9362 ± 0.0125 for the training cohort respectively, and 0.8984 ± 0.0536, 0.8671 ± 0.0489, 0.8500 ± 0.0599, 0.9118 ± 0.0550 and 0.8882 ± 0.0440 for the validation cohort respectively (reported as mean ± standard deviation). It shows a better discrimination capability than single-view methods, our previous method, and eight baseline methods. When the dimension was reduced to 2, the model not only has effective prediction performance, but also is convenient for data visualization. Conclusions. Our proposed method by integrating radiomics features of primary tumor and LN can be helpful in predicting lymph node metastasis in patients of GC. It shows multi-view learning has great potential for guiding the prognosis and treatment decision-making in GC. 

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