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%.     

2019年重庆市30岁及以上人群归因于被动吸烟的肺癌疾病负担研究

目的 分析重庆市肺癌发病死亡和疾病负担归因于被动吸烟的情况,为开展肺癌防治提供建议。 方法 肺癌死亡个案数据来源于2019年重庆市肿瘤登记报告系统,被动吸烟率来自2013年重庆市慢性病及危险因素监测。计算人群归因危险度百分比(population attributable risk percent, PAR%)、被动吸烟导致的肺癌发病、死亡和疾病负担。采用Excel 2010与SPSS 25.0进行统计分析,率的比较采用χ²检验。 结果 2013年30岁及以上成年人被动吸烟率为52.37%。2019年重庆市30岁及以上人群肺癌发病率与标化发病率分别为118.44/10万与80.83/10万,死亡率与标化死亡率分别为96.51/10万、63.58/10万。肺癌发病率和死亡率归因于被动吸烟的PAR%分别为19.76和19.04,归因发病率与归因标化发病率分别为23.41/10万和16.34/10万,归因死亡率与归因标化死亡率分别为18.38/10万和12.40/10万。2019年重庆市30岁及以上肺癌早死所致寿命损失年率(years of life lost,YLL)、残疾所致寿命损失年率(years lived with disability,YLD)、调整伤残寿命损失年率(disability adjusted life year,DALY)分别为21.16‰、0.31‰、21.47‰,YLL率、YLD率、DALY率归因于被动吸烟的PAR%分别为21.16、19.76和20.49,归因YLL率为4.34‰,归因YLD率为0.06‰,归因DALY率为4.40‰。 结论 2019年重庆市30岁及以上人群肺癌发病率、死亡率、YLL率、DALY率高,被动吸烟率高,肺癌归因于被动吸烟的疾病负担重,应加强落实控烟工作。     

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.     

Sex Disparities in Risk of Mortality Among Children With ESRD

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Opportunities and barriers to effective operation and maintenance of public toilets in informal settlements: perspectives from toilet operators in Kampala

Although classified by the Joint Monitoring Programme (JMP) as unimproved sanitation facilities, public toilets still play a critical role in eliminating open defecation in informal settlements. We explored perspectives of toilet operators on opportunities and barriers to operation and maintenance (O&M) of public toilets in informal settlements. A cross-sectional study design was used. Up to 20 in-depth interviews were used to obtain data on the experiences of public toilet operators. Thematic content analysis was used.

Ressults show that opportunities for improving O&M include; operation of public toilets is a source of livelihood; operators are knowledgeable on occupational risks, and the community is involvedin sanitation activities. Barriers to effective O&M include; high operation costs, failure to break even and a lack of investments in occupational health Therefore, there is need to recognise the significance of public toilets as a viable alternative to open defecation in areas where ownership of private sanitation facilities is difficult. Failure to observe the health and safety of toilet operators may further compromise O&M.     

Risk communication in a patient decision aid for radiotherapy in breast cancer: How to deal with uncertainty?

Background and aimPatient decision aids for oncological treatment options, provide information on the effect on recurrence rates and/or survival benefit, and on side-effects and/or burden of different treatment options. However, often uncertainty exists around the probability estimates for recurrence/survival and side-effects which is too relevant to be ignored. Evidence is lacking on the best way to communicate these uncertainties. The aim of this study is to develop a method to incorporate uncertainties in a patient decision aid for breast cancer patients to support their decision on radiotherapy.MethodsFirstly, qualitative interviews were held with patients and health care professionals. Secondly, in the development phase, thinking aloud sessions were organized with four patients and 12 health care professionals, individual and group-wise.ResultsConsensus was reached on a pictograph illustrating the whole range of uncertainty for local recurrence risks, in combination with textual explanation that a more exact personalized risk would be given by their own physician. The pictograph consisted of 100 female icons in a 10 x 10 array. Icons with a stepwise gradient color indicated the uncertainty margin. The prevalence and severity of possible side-effects were explained using verbal labels.ConclusionsWe developed a novel way of visualizing uncertainties in recurrence rates in a patient decision aid. The effect of this way of communicating risk uncertainty is currently being tested in the BRASA study (NCT03375801).