Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome.

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature.

A quantile-slicing approach for sufficient dimension reduction with censored responses.

Sufficient dimension reduction (SDR) that effectively reduces the predictor dimension in regression has been popular in high-dimensional data analysis. Under the presence of censoring, however, most existing SDR methods suffer. In this article, we propose a new algorithm to perform SDR with censored responses based on the quantile-slicing scheme recently proposed by Kim et al. First, we estimate the conditional quantile function of the true survival time via the censored kernel quantile regression (Shin et al.) and then slice the data based on the estimated censored regression quantiles instead of the responses. Both simulated and real data analysis demonstrate promising performance of the proposed method.

Manufacturing data analytics using a virtual factory representation.

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Probability-enhanced sufficient dimension reduction for binary classification.

In high-dimensional data analysis, it is of primary interest to reduce the data dimensionality without loss of information. Sufficient dimension reduction (SDR) arises in this context, and many successful SDR methods have been developed since the introduction of sliced inverse regression (SIR) [Li (1991) Journal of the American Statistical Association 86, 316-327]. Despite their fast progress, though, most existing methods target on regression problems with a continuous response. For binary classification problems, SIR suffers the limitation of estimating at most one direction since only two slices are available. In this article, we develop a new and flexible probability-enhanced SDR method for binary classification problems by using the weighted support vector machine (WSVM). The key idea is to slice the data based on conditional class probabilities of observations rather than their binary responses. We first show that the central subspace based on the conditional class probability is the same as that based on the binary response. This important result justifies the proposed slicing scheme from a theoretical perspective and assures no information loss. In practice, the true conditional class probability is generally not available, and the problem of probability estimation can be challenging for data with large-dimensional inputs. We observe that, in order to implement the new slicing scheme, one does not need exact probability values and the only required information is the relative order of probability values. Motivated by this fact, our new SDR procedure bypasses the probability estimation step and employs the WSVM to directly estimate the order of probability values, based on which the slicing is performed. The performance of the proposed probability-enhanced SDR scheme is evaluated by both simulated and real data examples.

Continuous high-pressure negative suction drain: new powerful tool for closed wound management: clinical experience.

BACKGROUND: Although various reconstructive flap surgeries have been successfully performed, there still are difficult wound complications, such as seroma formation, wound margin necrosis, delayed wound healing, and even flap failures. The negative-pressure wound therapy has been described in detail in the literature to assist open chronic/complex wound closure in reconstructive surgery. However, the negative-pressure wound therapy was difficult to be applied under the incisional closed wounds. METHODS: A total of 23 patients underwent the various reconstructive flap surgeries with continuous high-pressure negative suction drain. Instead of using regular suction units, Barovac (50-90 mm Hg, Sewoon Medical, Seoul, Republic of Korea) drainage tubes were connected to the wall suction unit, providing continuous high-powered negative pressure. In addition, continuous subatmospheric suction pressure (100-300 mm Hg) was applied. Outcome of the measures was obtained from the incidence of seroma, volume of postoperative drainage, hospitalization period, and incidence of other typical wound complications. Dead space was evaluated postoperatively with ultrasonography. RESULTS: Using continuous high-pressure negative suction drain, successful management of seroma was obtained without any major complication such as wound infection, flap loss, and wound margin necrosis, except for only 1 case of seroma after discharge from the hospital. The indwelling time of the drain in the latissimus dorsi donor site was significantly reduced in comparison with the authors' previous data (P = 0.047). The volume of drainage and hospitalization period were also reduced; however, these were not statistically significant. The dead space with continuous high-pressure negative suction drain was more reduced than in the control group in the immediate postoperative period and confirmed with ultrasonography. CONCLUSIONS: Continuous high-pressure negative suction drain might be the simple and powerful solution in the management of challenging closed wounds.

Variable selection in large margin classifier-based probability estimation with high-dimensional predictors.

This is a discussion of the papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer.

The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model. This study was aimed to evaluate the performance of machine learning algorithm in predicting survival rates more than 5 years for individual patients with colorectal cancer. A total of 475 patients with colorectal cancer (CRC) and complete data who had underwent surgery for CRC were analyze to measure individual's survival rate more than 5 years using a machine learning based on penalized Cox regression. We conducted thorough calculations to measure the individual's survival rate more than 5 years for performance evaluation. The receiver operating characteristic curves for the LASSO penalized model, the SCAD penalized model, the unpenalized model, and the RSF model were analyzed. The least absolute shrinkage and selection operator penalized model displayed a mean AUC of 0.67 ±â€…0.06, the smoothly clipped absolute deviation penalized model exhibited a mean AUC of 0.65 ±â€…0.07, the unpenalized model showed a mean AUC of 0.64 ±â€…0.09. Notably, the random survival forests model outperformed the others, demonstrating the most favorable performance evaluation with a mean AUC of 0.71 ±â€…0.05. Compared to the conventional unpenalized Cox model, recent machine learning techniques (LASSO, SCAD, RSF) showed advantages for data interpretation.

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome.

Intra-tumor heterogeneity is an important driver of tumor evolution and therapy response. Advances in precision cancer treatment will require understanding of mutation clonality and subclonal architecture. Currently the slow computational speed of subclonal reconstruction hinders large cohort studies. To overcome this bottleneck, we developed Clonal structure identification through Pairwise Penalization, or CliPP, which clusters subclonal mutations using a regularized likelihood model. CliPP reliably processed whole-genome and whole-exome sequencing data from over 12,000 tumor samples within 24 hours, thus enabling large-scale downstream association analyses between subclonal structures and clinical outcomes. Through a pan-cancer investigation of 7,827 tumors from 32 cancer types, we found that high subclonal mutational load (sML), a measure of latency time in tumor evolution, was significantly associated with better patient outcomes in 16 cancer types with low to moderate tumor mutation burden (TMB). In a cohort of prostate cancer patients participating in an immunotherapy clinical trial, high sML was indicative of favorable response to immune checkpoint blockade. This comprehensive study using CliPP underscores sML as a key feature of cancer. sML may be essential for linking mutation dynamics with immunotherapy response in the large population of non-high TMB cancers.

New type of sauna-related burn: conductive contact burn.

A 70-year-old woman visited a Korean-style hot dry sauna room. The patient had a medical history of hypertension and hyperlipidemia. During the sauna, the patient slept for 30 minutes. During the sleep, the right medial thigh was covered with a fully wet towel. The patient sustained a second-degree burn on the right medial thigh area with multiple bullas. On physical examination, erythema, heating sensation, and swelling around the bullas were noted. The patient was admitted and received intravenous antibiotics for 7 days. A dressing with Silmazine 1% cream (sulfadiazine) was applied twice a day for prevention of local infection. The patient was discharged on day 14 without complication. In this case, the mechanism of the burn was different. Hot air has much thermal energy but is not conducted to the skin directly. A wet towel will have a relatively higher thermal capacity or heat capacity than a dry or damp towel, and the sodden water might be a medium for the conduction of thermal energy. Owing to the global popularity of sauna bathing, it is important to recognize all sources of sauna-related burns.

Dermatofibroma located on the tarsal plate after upper blepharoplasty?

Dermatofibroma is a firm, skin-colored or reddish-brown sessile papule or nodule. It arises spontaneously without a known cause or after a minor trauma, such as an insect bite. A 39-year-old Asian woman complained of a palpable mass and tenderness in the right upper eyelid. On physical examination, a firm palpable subcutaneous mass was detected in the upper eyelid. Surgical exploration under local anesthesia showed a fibrotic and firmly attached mass to the tarsal plate. In histopathologic examination, the mass was characterized by a fibroblast proliferation in the dermis with an overlying hyperplastic epidermis with frequent basal hyperpigmentation. As far as we know, there have been no previous reports of dermatofibroma located on the tarsal plate. This article is a good example for intratarsal location of dermatofibroma.

Long delayed traumatic carotid-cavernous sinus fistula.

Traumatic carotid-cavernous sinus fistula (TCCF) is a rare but significant vascular abnormality in the skull base found after craniomaxillofacial trauma. Although the direct type is usually caused by trauma, the onset of symptoms in TCCF may present several weeks after injury. We present the case of a patient who sustained a blunt head injury from falling down and was hospitalized with skull base fracture associated with zygomatic complex fractures on the right side. After surgery, the recovery was uneventful and the patient was discharged without any problems. On the eighth week postoperatively, the patient returned to hospital presenting tinnitus, bruit on the right orbital area, diplopia, eye pain, and headache. The patient also had severe limitation of ocular movement on lateral gaze. After having brain angio-CT, which showed a dilated superior ophthalmic vein, the diagnosis of CCF with cranial nerve VI was confirmed. The fistula was occluded successfully by using coils. After the procedure, tinnitus, bruit, and headache were reduced immediately. On following up 4 months after coiling, cranial nerve VI palsy and related symptoms-diplopia and limit of ocular movement-were improved significantly. Although TCCFs usually manifest symptoms early after trauma, in this case, the patient presented clinical signs 8 weeks post-injury, while the longest time that was acknowledged in another previously released article was 6 weeks. According to this case, we recommend a careful follow-up until 2 months for patients with skull base fracture in order to rule out the risk of CCF.

Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.

Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.

Closed reduction of fractured anterior wall of the frontal sinus using threaded K-wires.

Classic approach of the anterior wall of frontal sinus fracture involves bicoronal or direct forehead incisions. However, these methods have some disadvantages, for example, paresthesia, scarring, and even alopecia. The purpose of this study was to introduce the simple and effective method of reduction of the anterior wall of frontal sinus fracture with threaded K-wires. Thirteen patients with fracture of the anterior wall of the frontal sinus underwent our method from December 2005 to September 2011. The distance of threaded K-wires was measured by facial computed tomography, and those marks were made directly on the threaded K-wires with an aseptic pencil. Closed reduction with these K-wires was tried carefully until a tolerable level was attained with digital palpation. Facial computed tomography was performed the following day for comparison. On postoperative day 7, following disappearance of swelling, the patient and the doctor mutually evaluated the outcome; both were satisfied with the results. The postoperative course was uneventful, without any complications. Unlike other methods, this method can guarantee a short operating time, effective reduction, and inconspicuous scar.

Association between in vitro fertilization success rate and ambient air pollution: a possible explanation of within-year variation of in vitro fertilization success rate.

OBJECTIVE: To evaluate patterns in air pollution concentrations and in vitro fertilization (IVF) success rates using data from a large, long-term clinical database. METHODS: We conducted a retrospective cohort study investigating South Korean women who pursued IVF and embryo transfer (IVF-ET) between 2011 and 2017. Hourly concentrations of air pollutants measured at 318 air quality monitoring sites in South Korea between 2011 and 2017 were obtained from the National Institute of Environmental Research. Monthly trends in pregnancy rates and concentrations of air pollutants were assessed. RESULTS: A total of 34,427 IVF-ET cycles in 18,478 patients were analyzed. The mean age of women at the time of IVF-ET was 36.6 years. The clinical pregnancy rate in the IVF-ET cycle was 30%. Analysis of pregnancy failure rates by month showed that IVF-ET failure rates tended to be higher in March and April and lower in July and August. Concentrations of air pollutants including particulate matter (PM) less than 10 µm in diameter, PM less than 2.5 µm in diameter, sulfur dioxide, nitrogen dioxide, and carbon monoxide were highest in March and April and lowest between July and September. CONCLUSION: Within-year variations were similar between IVF-ET failure rates and air pollution concentrations based on analysis of a large, long-term database. Specifically, IVF-ET success rates were highest when PM concentrations were lowest. Further studies are warranted to examine the mechanisms accounting for the association between IVF success and air pollutant exposure.

Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.

Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with TP53 germline mutations and an increased lifetime risk of multiple primary cancers (MPC). Penetrance estimation of time to first and second primary cancer within LFS remains challenging because of limited data and the difficulty of characterizing the effects of a primary cancer on the penetrance of a second primary cancer. Using a recurrent events survival modeling approach that incorporates a family-wise likelihood to efficiently integrate the pedigree structure, we estimated the penetrance for both first and second primary cancer diagnosis from a pediatric sarcoma cohort at MD Anderson Cancer Center [MDACC, Houston, TX; number of families = 189; single primary cancer (SPC) = 771; and MPC = 87]. Validation of the risk prediction performance was performed using an independent MDACC clinical cohort of TP53 tested individuals (SPC = 102 and MPC = 58). These findings showed that an individual diagnosed at a later age was more likely to be diagnosed with a second primary cancer. In addition, TP53 mutation carriers had a HR of 1.65 (95% confidence interval, 1.1-2.5) for developing a second primary cancer versus SPC. The area under the ROC (AUC) curve for predicting individual outcomes of MPC versus SPC was 0.77. In summary, we provide the first set of penetrance estimates for first and second primary cancer for TP53 germline mutation carriers and demonstrate its accuracy for cancer risk assessment. SIGNIFICANCE: These findings present an open-source R package LFSPRO that could be used for genetic counseling and health management of individuals with LFS as it estimates the risk of both first and second primary cancer diagnosis.See related article by Shin et al., p. 354.

Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts.

Li-Fraumeni syndrome (LFS) is a rare hereditary cancer syndrome associated with an autosomal-dominant mutation inheritance in the TP53 tumor suppressor gene and a wide spectrum of cancer diagnoses. The previously developed R package, LFSPRO, is capable of estimating the risk of an individual being a TP53 mutation carrier. However, an accurate estimation of the penetrance of different cancer types in LFS is crucial to improve the clinical characterization and management of high-risk individuals. Here, we developed a competing risk-based statistical model that incorporates the pedigree structure efficiently into the penetrance estimation and corrects for ascertainment bias while also increasing the effective sample size of this rare population. This enabled successful estimation of TP53 penetrance for three LFS cancer types: breast (BR), sarcoma (SA), and others (OT), from 186 pediatric sarcoma families collected at MD Anderson Cancer Center (Houston, TX). Penetrance validation was performed on a combined dataset of two clinically ascertained family cohorts with cancer to overcome internal bias in each (total number of families = 668). The age-dependent onset probability distributions of specific cancer types were different. For breast cancer, the TP53 penetrance went up at an earlier age than the reported BRCA1/2 penetrance. The prediction performance of the penetrance estimates was validated by the combined independent cohorts (BR = 85, SA = 540, and OT = 158). Area under the ROC curves (AUC) were 0.92 (BR), 0.75 (SA), and 0.81 (OT). The new penetrance estimates have been incorporated into the current LFSPRO R package to provide risk estimates for the diagnosis of breast cancer, sarcoma, or other cancers. SIGNIFICANCE: These findings provide specific penetrance estimates for LFS-associated cancers, which will likely impact the management of families at high risk of LFS.See related article by Shin et al., p. 347.

Simulating a Virtual Machining Model in an Agent-Based Model for Advanced Analytics.

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called "virtual manufacturing". The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls (STEP-NC) and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level.

Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome.

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a high-risk group develop different cancers, and accommodate family data using family-wise likelihoods. We tackle the ascertainment bias arising when family data are collected through probands in a high-risk population in which disease cases are more likely to be observed. We apply the proposed method to a cohort of 186 families with Li-Fraumeni syndrome identified through probands with sarcoma treated at MD Anderson Cancer Center from 1944 to 1982.

Principal weighted support vector machines for sufficient dimension reduction in binary classification.

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification.