Purpose:To develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate Grade1 and Grade2/3 tumors in patients with pancreatic neuroendocrine tumors (pNETs). Experimental Design:A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n=51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and LASSO regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve(AUC), calibration curve and decision curve analysis(DCA). The Kaplan-Meier(KM) analysis was employed for survival analysis. Results: An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC=0.907; validation set: AUC=0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted Grade1 and Grade2/3 groups (p=0.002). Conclusions:The combined nomogram model developed could be useful in differentiating Grade1 and Grade2/3 tumor in patients with pNETs.

Simultaneous imaging of multiple probes or biomarkers represents a critical step toward high specificity molecular imaging. In this work, we propose to utilize the element-specific nature of the X-ray fluorescence (XRF) signal for imaging multiple elements simultaneously (multiplexing) using XRF computed tomography (XFCT). A 5-mm-diameter pencil beam produced by a polychromatic X-ray source (150 kV, 20 mA) was used to stimulate emission of XRF photons from 2% (weight/volume) gold (Au), gadolinium (Gd), and barium (Ba) embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The X-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used to isolate the K shell XRF peak and to generate sinograms for the three elements of interest. The distribution and concentration of the three elements were reconstructed with the iterative maximum likelihood expectation maximization algorithm. The linearity between the XFCT intensity and the concentrations of elements of interest was investigated. We found that measured XRF spectra showed sharp peaks characteristic of Au, Gd, and Ba. The narrow full-width at half-maximum (FWHM) of the peaks strongly supports the potential of XFCT for multiplexed imaging of Au, Gd, and Ba (FWHM _Au,Kα1 = 0.619 keV, FWHM _Au,Kα2 =1.371 keV , FWHM _Gd,Kα =1.297 keV, FWHM _Gd,Kβ =0.974 keV , FWHM _Ba,Kα =0.852 keV, and FWHM _Ba,Kβ =0.594 keV ). The distribution of Au, Gd, and Ba in the water phantom was clearly identifiable in the reconstructed XRF images. Our results showed linear relationships between the XRF intensity of each tested element and their concentrations (R ² _Au =0.944 , R _Gd ² =0.986, and R _Ba ² =0.999), suggesting that XFCT is capable of quantitative imaging. Finally, a transmission CT image was obtained to show the potential of the approach for providing attenuation correction and morphological information. In conclusion, XFCT is a promising modality for multiplexed imaging of high atomic number probes.

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

Purpose:: Developing an imaging method to directly monitor the spatial distribution of platinum‐based (Pt) drugs at the tumor region is of critical importance for early assessment of treatment efficacy and personalized treatment. In this study, the authors investigated the feasibility of imaging platinum (Pt)‐based drug distribution using x‐ray fluorescence (XRF, a.k.a. characteristic x ray) CT (XFCT).Methods:: A 5‐mm‐diameter pencil beam produced by a polychromatic x‐ray source equipped with a tungsten anode was used to stimulate emission of XRF photons from Pt drug embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first‐generation CT geometry. The x‐ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used for the K‐shell XRF peak isolation and sinogram generation for Pt. The distribution and concentration of Pt were reconstructed with an iterative maximum likelihood expectation maximization algorithm. The capability of XFCT to multiplexed imaging of Pt, gadolinium (Gd), and iodine (I) within a water phantom was also investigated.Results:: Measured XRF spectrum showed a sharp peak characteristic of Pt with a narrow full‐width at half‐maximum (FWHM) (FWHM_Kα1 = 1.138 keV, FWHM_Kα2 = 1.052 keV). The distribution of Pt drug in the water phantom was clearly identifiable on the reconstructed XRF images. Our results showed a linear relationship between the XRF intensity of Pt and its concentrations (R² = 0.995), suggesting that XFCT is capable of quantitative imaging. A transmission CT image was also obtained to show the potential of the approach for providing attenuation correction and morphological information. Finally, the distribution of Pt, Gd, and I in the water phantom was clearly identifiable in the reconstructed images from XFCT multiplexed imaging.Conclusions:: XFCT is a promising modality for monitoring the spatial distribution of Pt drugs. The technique may be useful in tailoring tumor treatment regimen in the future.

Purpose: Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients.Methods and Materials: 214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis.Results: The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated.Conclusions: A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

The principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy. Eight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues. Our results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met. Incorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.