This is the first study to describe the outcomes of 0-7-21 in treating advanced HNCs. The positive results suggest that 0-7-21 provides excellent palliation with minimal toxicity, with significantly less on-treatment time than current published palliative RT regimen.
In this paper the vertex weight is introduced into a snowdrift game to study the evolution of cooperative behavior. Compared with the snowdrift game in a traditional square lattice without any weight, cooperation can be promoted under three types of weight distribution: uniform, exponential and power-law distribution. For an intermediate cost-to-benefit ratio (r ), in particular, the facilitation effect of cooperation is obvious. Moreover, the influence of undulation amplitude of weight distribution and the noise strength of strategy selection on cooperative behavior are also investigated. They exhibit a nontrivial phenomenon as a function of r . The results are helpful in analyzing and understanding the emergence of collective cooperation that is found widely in many natural and social systems.
Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium‐enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre‐treatment, 1‐month and 3‐month post‐CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial‐temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection, whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression‐free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave‐one‐out cross‐validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can accurately predict survival in GBM patients treated with CRT.
Purpose: To investigate the outcome predictive power of tumor volume measured by serial MR imaging (MRI) of cervical cancer, including the sensitivity and specificity to identify patients at risk of local failure. Method and Materials: Seventy‐nine patients with cervical cancer stages IB2‐IVA, treated with radiation/chemotherapy (RT/CT), underwent serial MRI: MRI 1(pre‐RT), MRI 2(at 20–25 Gy/2 weeks), MRI 3(at 40–50 Gy/4 weeks), and MRI 4(at 1–2 months post‐RT). Mean follow up was 6.2 (0.2–9.4) years. Tumor volumes (V1,V2,V3,V4) and regression ratios (V2/V1,V3/V1,V4/V1) were measured by MRI 3D volumetry, and correlated with local tumor‐control and disease‐free survival using Mann‐Whitney rank‐sum test. Results: The volume data collected in this study were analyzed and the predictive power in terms of p‐value to discriminate local tumor‐control and disease‐free survival was computed. The absolute tumor volumes (V2,V3,V4) and the regression ratios (V2/V1,V3/V1,V4/V1) strongly correlated with local tumor‐control (p<0.001). These parameters also correlated with disease‐free survival, but only the last measurement (MRI 4) showed significant predictive value (p=0.02). Four methods had been developed to identify patients at risk for tumor recurrence (sensitivity 61%–100% and specificity 87%–100%). The most powerful method is based on the volume regression measured in MRI 3 and MRI 4 (V3/V1 >20% and V4/V1 >10%), which have a sensitivity of 89% and a specificity of 100%. Local failure can also be predicted as early as 2–3 weeks (MRI 2), the method of V1 >40 cc and V2/V1 >75% shows a sensitivity of 61% and a specificity of 93%. Conclusion: MRI‐based volumetric tumor measurement provides important predictive information about tumor response to the ongoing RT/CT. The methods developed in this study demonstrate a high specificity (87%–100%) for patients at risk of local failure based on long‐term follow‐up. These methods may classify patients who require more aggressive therapeutic intervention.
Purpose: The quantitative imaging analysis of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) data is subject to errors caused by motion during the image acquisition. Previous studies on the pixel‐by‐pixel analysis of perfusion DCE‐MRI for cervical cancer ignored this motion effect or corrected it manually. To improve the quantification of tumor perfusion, a comprehensive analysis of the tumor motion was conducted in this work by automatically registering the time series of DCE‐MRI. Methods and Materials: Fifty‐six patients and a total of 192 MR examinations were included in the registration study. To improve image quality, before registration the image noise was first reduced by employing edge preserving smoothing techniques. A mask region including uterus, bladder and cervix was applied to all time‐serial slices so that both internal organ motion and patient body movement were taken into account in the image registration. The registration was then performed by rigid translation using the normalized mutual information (NMI) algorithm to compensate respiratory and bowel motion, followed by non‐rigid transformation using the Demons algorithm to compensate for deformation within the slice plane. Results: For all 56 patients and 192 studies, most of slices (80%) in each study have ±1 mm or less translational displacement from registration to the reference image, and 20% of the slices have a ⩾ 2 mm translational displacement. In most slices (80%), the absolute motion is approximately 1 pixel (1.526 mm) and 10% of slices have more than 3‐pixel motion (⩾ 4–5 mm). Conclusions: Movements at the level larger than the pixel size will lead to pixel misalignment in a time series, consequently will influence the pixel‐based analysis of tumor perfusion data. Therefore, accounting for and correcting tumor motion effects by image registration may improve the results of the pixel‐based analysis of DCE‐MRI data for cervical cancer.
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