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.     

胎儿小脑蚓部不同超声纹理特征与胎儿畸形关系的初探郭永华 肖宝军

目的：总结后颅窝正常和异常的胎儿小脑及小脑蚓部的矢状面的声像学特征,了解小脑细微结构的改变与胎儿畸形的关系。     

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.     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

经食管实时三维超声心动图评价房性心律失常对二尖瓣结构和功能的影响

目的:应用经食管实时三维超声心动图(RT-3D-TEE)技术,探讨房性心律失常对二尖瓣结构和功能的影响.方法:选取2018年6月~2019年6月本院收治的房性心律失常拟行射频消融的患者49例纳入观察组,另外选取21例正常窦性心律者作为对照组,所有患者均行经胸超声心电图(TTE)和经食管实时三维超声心动图(RT-3D-TEE)检查,将两组患者的瓣环投影面积(A2D)、瓣环周长(C3D)、瓣环高度(H)、瓣环前后径(DAP)、瓣环前外侧至后内侧直径(DAIPm)、左室射血分数(LVEF)、左心房前后径(Lad)等参数进行比较,并分别计算对合面积和对合指数,之后采取二项Logistic回归分析或逐步线性回归对各参数及临床因素与对合指数的相关性进行分析.结果:两组患者在LVEF和对合面积方面的比较无明显差异(P>0.05),在A2D、C3D、H、DA、PDAIPm、Lad方面的比较,房性心律失常组患者明显高于正常窦性心律组患者(P<0.05),且观察组患者的对合指数明显降低(P<0.05).将49例观察组患者以不同心律失常类型分为持续性房颤、阵发性房颤、房扑、混合型房性心律失常4个亚组,采用单因素方差分析,发现在LVEF、C3D、Lad、对合指数方面的比较,各亚组间无明显差异(P>0.05).采用二项Logistic回归分析发现,女性是导致对合指数低的危险因素,另外对合指数较低、房性心律失常是导致二尖瓣返流的重要因素.结论:房性心律失常对患者二尖瓣的A2D、C3D、H、DA、PDAIPm、Lad等都会造成较大影响,从而降低对合指数,影响二尖瓣的正常功能,导致二尖瓣返流的发生.     

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。