A Proposal for an Alternative Approach to Particle Size Method Development During Early-Stage Small Molecule Pharmaceutical Development

Particle size analysis in the pharmaceutical industry has long been a source of debate regarding how best to define measurement accuracy; the degree to which the result of a measurement or calculation conforms to the true value. Defining a “true” value for the size of a particle can be challenging as the output of its measurement will differ because of variations in measurement approaches, instrumental differences and calculation methods. Consequently, for “real” particles, a universal “true” value does not exist and accuracy is therefore not a definable characteristic. Accordingly, precision is then a measure of the ability to reproducibly achieve a measurement of unknown relevance.This article proposes, in place of accuracy, a means to define the “appropriateness” of a measurement in line with the critical quality attributes (CQA) of the material being characterized. The decision as to whether the measurement is correct should involve a link to the CQA; that is, correlation should be demonstrated, without which the measured particle size cannot be defined as a critical material attribute.Correspondingly, methods should also be able to provide sufficient precision to demonstrate discrimination relating to variation in the CQA. The benefits and challenges of this approach are discussed.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Effect of Day Optimizing Throughput (Dot) Knee Software Implementation on Magnetic Resonance Imaging Workflow Efficiency

PurposeAttempts by magnetic resonance (MR) manufacturers to help imaging centres improve patient throughput has led to the development of more automated acquisition. This software is capable of customizing individual scan alignment; potentially improving imaging efficiency and standardizing protocols. However, substantial investments are required to introduce such systems, potentially deterring their widespread application. This study assessed the implementation costs and reduction in examination durations for automated knee MR imaging (MRI) software.Materials and MethodsResearch activities were performed at a community-based academic centre on a 3-Tesla (3-T) system using Siemens' Day Optimizing Throughput (Dot) knee software. Examination acquisition times were extracted from the system before and after software implementation. Fiscal year 2012/13 finances were used to determine the average hourly cost of MRI utilization. Costs associated with automated software implementation were also calculated. Finally, the number of knee scans required to achieve a positive return on investment using the software was established.Results and DiscussionThe mean (standard deviation, sample size) pre- and post-Dot software scan times were 23.20 (4.18, n = 266) and 21.94 (4.51, n = 59) minutes, respectively, for a routine knee scan and 11.88 (1.60, n = 74) and 11.24 (1.51, n = 27) minutes, respectively, for a fast knee scan. The overall weighted average resulted in a 64-second time savings per automated knee examination. This negligible time savings would be extremely difficult to make use of clinically. Dot simplified 29 unique knee protocols to two, improving the consistency of knee examinations. Current Dot software is not compatible with all patients and therefore has limitations that are a concern among MR technologists.ConclusionAdoption of automated knee systems could assist in standardizing protocols; however, the cost of implementation and difficulty in modifying patient scheduling to reflect the minimal time savings would make a financial return unlikely to occur at small- and medium-sized institutions.     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

H型高血压患者舌面诊图像颜色特征参数分析＊

目的 分析H型高血压患者的舌面诊图像颜色参数特征，探讨H型高血压患者的舌诊、面诊变化规律。方法 运用上海中医药大学自行研制的Smart TCM-1型中医舌面一体仪，采集高血压患者舌面诊图像，提取特征参数，分析健康对照组、H型高血压组与非H型高血压组患者舌面颜色参数特征。结果 ①在舌色各项参数中，H型高血压组舌尖部R值、B值、V值均显著小于健康对照组（P < 0.01）；非H型高血压组舌尖部B值显著小于健康对照组（P < 0.01），S值较健康对照组显著增大（P < 0.05）；H型高血压组舌尖部R、V值均明显小于非H型高血压组（P < 0.05）。在舌苔各项参数中，H型高血压组舌中H值、V值均明显小于健康对照组（P < 0.05）；非H型高血压组舌中V值、舌右V值均显著小于健康对照组（P < 0.01）；H型高血压组舌中H值明显小于非H型高血压组（P < 0.05），右侧舌苔S值明显大于非H型高血压组（P < 0.05）。②H型高血压组面色参数鼻G值、下颌G值、口唇R值、口唇V值均明显小于健康对照组（P < 0.05）；非H型高血压组前额H值、目眶H值、脸颊H值、鼻H值、下颌H值、整体H值均明显大于健康对照组（P < 0.05）；H型高血压组前额H值、目眶G值、目眶H值、脸颊H值、鼻G值、鼻H值、下颌R值、下颌G值、下颌H值、下颌V值、口唇R值、口唇G值、口唇V值、整体R值、整体G值、整体H值、整体V值均明显小于非H型高血压组（P < 0.05）。结论 H型高血压患者苔色偏黄，以舌中部为主，且舌右侧黄苔积聚较明显；H型高血压患者面色为黄中带红，口唇、下颌部更为晦暗。H型高血压患者的舌、面诊特征参数的变化，与高血压病阳亢湿盛病机相符。     

Free Water Loss–induced Heterogeneous Residual Strain and Reduced Fatigue Resistance in Root Dentin: A 3-dimensional Digital Image Correlation Analysis

IntroductionThis study evaluated free water loss–induced residual strain with and without axial compressive loading and assessed the mechanical effect of cyclic loading in fully hydrated and partially dehydrated root dentin.MethodsRoot dentin sections prepared from freshly extracted human premolars were used. Customized 3-dimensional digital image correlation was used to qualitatively and quantitatively analyze the residual strain induced by 2 hours of free water loss in different regions of root dentin. Residual strain in partially dehydrated root dentin during axial compressive loading was also analyzed using 3-dimensional digital image correlation. The effect of cyclic loading on load to fracture in fully hydrated and partially dehydrated dentin and their fractography were analyzed using micro–computed tomographic imaging.ResultsFree water loss resulted in a heterogeneous distribution of residual strain and an overall formation of residual compressive strain with areas of tensile strain localized to the root canal and outer dentin. More residual compressive strain was observed in the apical dentin compared with the cervical dentin (P < .05), and more residual shear strain was observed in outer dentin compared with inner dentin (P < .05). Axial loading resulted in an increase in the load-induced compressive strain in the direction perpendicular to dentinal tubules (P < .05). Fully hydrated roots displayed a higher mean (P < .05) and median (P < .05) number of cycles to fracture with microcracks characteristic of toughness.ConclusionsAfter free water loss, root dentin displayed an increased formation of heterogenous residual strain, which resulted in increased axial compressive load-induced strain and a decreased resistance to fatigue failure. The effect of free water loss in the loss of mechanical integrity of root-filled teeth needs further investigation.     

Ultrasound-guided Brachytherapy for Cervix Cancer

Radiotherapy and brachytherapy are the definitive treatments for locally advanced cervix cancer. The use of soft-tissue imaging, particularly magnetic resonance imaging, has enhanced their effectiveness and improved clinical outcomes. However, the use of magnetic resonance imaging is largely restricted to well-resourced centres in both the first and developing world and remains elusive to many less advantaged centres, particularly those in areas with a high burden of cervix cancer. Ultrasound is an accessible, affordable and accurate imaging modality that can be used throughout the brachytherapy procedure. Ultrasound is primarily used to ensure safe insertion of the applicator but can also be used to guide planning. The methods used to utilise ultrasound images for planning are described. Ultrasound is particularly useful as a verification aid to confirm applicator placement after patients are moved and transferred around the radiotherapy department. It can also be used to verify the dimensions of treatment volumes over the course of brachytherapy. There is a crucial unmet need for an accessible economical soft-tissue imaging modality in cervical brachytherapy. Ultrasound has the potential to meet this need.     

采用半透明效果实现医学三维整体可视化

目的通过改进直接体绘制技术（DVR）算法中的关键步骤，绘制能表达数据场中不同层次组织的内部结构。方法根据界面对光线的反射和物质本身对光线的衰减，将数据场划分为边界不丰富的简单数据场和边界丰富的复杂数据场，从而采用分段线性函数来构造阻光度传递函数（TF）；将数据场中低灰度体素点看为一种不发光但会导致采样光线衰减的物质；根据采样结束时采样光线的阻光度分布来调整TF，完成三维绘制。结果绘制出了不同层次组织的内部结构。结论本算法有效地解决了体素点相互遮挡的问题，实现了医学三维整体可视化。