Somatic Mutations in Surgically Treated Colorectal Liver Metastases: An Overview.

Colorectal cancer is the second most common cause of cancer death in the United States, and up to half of patients develop colorectal liver metastases (CRLMs). Notably, somatic genetic mutations, such as mutations in RAS, BRAF, mismatch repair (MMR) genes, TP53, and SMAD4, have been shown to play a prognostic role in patients with CRLM. This review summarizes and appraises the current literature regarding the most relevant somatic mutations in surgically treated CRLM by not only reviewing representative studies, but also providing recommendations for areas of future research. In addition, advancements in genetic testing and an increasing emphasis on precision medicine have led to a more nuanced understanding of these mutations; thus, more granular data for each mutation are reviewed when available. Importantly, such knowledge can pave the way for precision medicine with the ultimate goal of improving patient outcomes.

Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery. METHODS: A systematic search of Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar was performed through December 2022. The primary outcome was the discriminatory performance of ML vs LR models as measured by the area under the receiver operating characteristic curve (AUC). A meta-analysis was then performed using a random effects model. RESULTS: A total of 62 LR models and 143 ML models were included across 38 studies. On average, the best-performing ML models had a significantly higher AUC than the LR models (ΔAUC, 0.07; 95% CI, 0.04-0.09; P < .001). Similarly, on average, the best-performing ML models had a significantly higher logit (AUC) than the LR models (Δlogit [AUC], 0.41; 95% CI, 0.23-0.58; P < .001). Approximately half of studies (44%) were found to have a low risk of bias. Upon a subset analysis of only low-risk studies, the difference in logit (AUC) remained significant (ML vs LR, Δlogit [AUC], 0.40; 95% CI, 0.14-0.66; P = .009). CONCLUSION: We found a significant improvement in discriminatory ability when using ML over LR algorithms in predicting postoperative outcomes for patients undergoing GI surgery. Subsequent efforts should establish standardized protocols for both developing and reporting studies using ML models and explore the practical implementation of these models.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Incidence of hypocortisolism with long-term budesonide irrigation for chronic rhinosinusitis.

BACKGROUND: Budesonide irrigations (BIs) are commonly used to control inflammation in chronic rhinosinusitis (CRS). In 2016 we reported an analysis of long-term BI with regard to hypothalamic-pituitary-adrenal axis function. We present a follow-up analysis in a larger cohort of patients with longer follow-up. METHODS: Patients were candidates for stimulated cortisol testing after regularly performing BI for CRS at least daily for ≥6 months. We retrospectively evaluated all patients who received stimulated cortisol testing at our center between 2012 and 2022. We correlated cortisol levels with the use of BI and other forms of corticosteroids. RESULTS: We analyzed 401 cortisol test results in 285 patients. The mean duration of use was 34 months. Overall, 21.8% of patients were hypocortisolemic (<18 ug/dL) at first test. In patients who used only BI, the rate of hypocortisolemia was 7.5%, whereas in patients who also used concurrent oral and inhaled corticosteroids, the rate was 40% to 50%. Lower cortisol levels were associated with male sex (p < 0.0001) and concomitant use of oral and inhaled steroids (p < 0.0001). Duration of BI use was not significantly associated with lower cortisol levels (p = 0.701), nor was greater dosing frequency (p = 0.289). CONCLUSION: Prolonged use of BI alone is not likely to cause hypocortisolemia in the majority of patients. However, concomitant use of inhaled and oral steroids and male sex may be associated with hypocortisolemia. Surveillance of cortisol levels may be considered in vulnerable populations who use BI regularly, particularly in patients using other forms of corticosteroids with known systemic absorption.

Is Precision Surgery Applicable to Colorectal Liver Metastases? A Systematic Review and Meta-analysis of Studies that Investigate the Association of Surgical Technique with Outcomes in the Context of Distinct Tumor Biology.

BACKGROUND: Although some data suggest that patients with mutRAS colorectal liver metastases (CRLM) may benefit from anatomic hepatectomy, this topic remains controversial. We performed a systematic review and meta-analysis to determine whether RAS mutation status was associated with prognosis relative to surgical technique [anatomic resection (AR) vs. nonanatomic resection (NAR)] among patients with CRLM. PATIENTS AND METHODS: A systematic review and meta-analysis of studies were performed to investigate the association of AR versus NAR with overall and liver-specific disease-free survival (DFS and liver-specific DFS, respectively) in the context of RAS mutation status. RESULTS: Overall, 2018 patients (831 mutRAS vs. 1187 wtRAS) were included from five eligible studies. AR was associated with a 40% improvement in liver-specific DFS [hazard ratio (HR) = 0.6, 95% confidence interval (CI) 0.44-0.81, p = 0.01] and a 28% improvement in overall DFS (HR = 0.72, 95% CI 0.54-0.95, p = 0.02) among patients with mutRAS tumors; in contrast, AR was not associated with any improvement in liver-specific DFS or overall DFS among wtRAS patients. These differences may have been mediated by the 40% decreased incidence in R1 resection among patients with mutRAS tumors who underwent AR versus NAR [relative risk (RR): 0.6, 95% CI 0.40-0.91, p = 0.02]. In contrast, the probability of an R1 resection was not decreased among wtRAS patients who underwent AR versus NAR (RR: 0.93, 95% CI 0.69-1.25, p = 0.62). CONCLUSIONS: The data suggest that precision surgery may be relevant to CRLM. Specifically, rather than a parenchymal sparing dogma for all patients, AR may have a role in individuals with mutRAS tumors.

A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA.

The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as "empty"). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions.

Meta-Learned Models of Cognition.

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.

An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study.

Background: There are several models that predict the risk of recurrence following resection of localised, primary gastrointestinal stromal tumour (GIST). However, assessment of calibration is not always feasible and when performed, calibration of current GIST models appears to be suboptimal. We aimed to develop a prognostic model to predict the recurrence of GIST after surgery with both good discrimination and calibration by uncovering and harnessing the non-linear relationships among variables that predict recurrence. Methods: In this observational cohort study, the data of 395 adult patients who underwent complete resection (R0 or R1) of a localised, primary GIST in the pre-imatinib era at Memorial Sloan Kettering Cancer Center (NY, USA) (recruited 1982-2001) and a European consortium (Spanish Group for Research in Sarcomas, 80 sites) (recruited 1987-2011) were used to train an interpretable Artificial Intelligence (AI)-based model called Optimal Classification Trees (OCT). The OCT predicted the probability of recurrence after surgery by capturing non-linear relationships among predictors of recurrence. The data of an additional 596 patients from another European consortium (Polish Clinical GIST Registry, 7 sites) (recruited 1981-2013) who were also treated in the pre-imatinib era were used to externally validate the OCT predictions with regard to discrimination (Harrell's C-index and Brier score) and calibration (calibration curve, Brier score, and Hosmer-Lemeshow test). The calibration of the Memorial Sloan Kettering (MSK) GIST nomogram was used as a comparative gold standard. We also evaluated the clinical utility of the OCT and the MSK nomogram by performing a Decision Curve Analysis (DCA). Findings: The internal cohort included 395 patients (median [IQR] age, 63 [54-71] years; 214 men [54.2%]) and the external cohort included 556 patients (median [IQR] age, 60 [52-68] years; 308 men [55.4%]). The Harrell's C-index of the OCT in the external validation cohort was greater than that of the MSK nomogram (0.805 (95% CI: 0.803-0.808) vs 0.788 (95% CI: 0.786-0.791), respectively). In the external validation cohort, the slope and intercept of the calibration curve of the main OCT were 1.041 and 0.038, respectively. In comparison, the slope and intercept of the calibration curve for the MSK nomogram was 0.681 and 0.032, respectively. The MSK nomogram overestimated the recurrence risk throughout the entire calibration curve. Of note, the Brier score was lower for the OCT compared to the MSK nomogram (0.147 vs 0.564, respectively), and the Hosmer-Lemeshow test was insignificant (P = 0.087) for the OCT model but significant (P < 0.001) for the MSK nomogram. Both results confirmed the superior discrimination and calibration of the OCT over the MSK nomogram. A decision curve analysis showed that the AI-based OCT model allowed for superior decision making compared to the MSK nomogram for both patients with 25-50% recurrence risk as well as those with >50% risk of recurrence. Interpretation: We present the first prognostic models of recurrence risk in GIST that demonstrate excellent discrimination, calibration, and clinical utility on external validation. Additional studies for further validation are warranted. With further validation, these tools could potentially improve patient counseling and selection for adjuvant therapy. Funding: The NCI SPORE in Soft Tissue Sarcoma and NCI Cancer Center Support Grants.

Development of hybrid scaffolds with biodegradable polymer composites and bioactive hydrogels for bone tissue engineering.

The development of treatments for critical-sized bone defects has been considered an important topic in the biomedical field because of the high demand for transplantable bone grafts. Following the concept of tissue engineering, implantation of biocompatible porous scaffolds carrying cells and regulating factors is the most efficient strategy to stimulate clinical bone regeneration. With the advancement in the development of 3D-printing techniques, scaffolds with highly controllable architectures can be fabricated to further improve healing efficacies. However, challenges such as the limited biocompatibility of resin materials and poor cell-carrying capacities still exist in the application of current scaffolds. In this study, a novel biodegradable polymer, poly (ethylene glycol)-co-poly (glycerol sebacate) acrylate (PEGSA), was synthesized and blended with hydroxyapatite (HAP) nanoparticles to produce osteoinductive and photocurable resins for 3D printing. The composites were optimized and applied in the fabrication of gyroid scaffolds with biomimetic characteristics and high permeability, followed by the combination of bioactive hydrogels containing Wharton's jelly-derived mesenchymal stem cells (WJMSC) to increase the efficiency of cell delivery. The promotion of osteogenesis from 3D-printed scaffolds was confirmed in-vivo while the hybrid scaffolds were proven to be great platforms for WJMSC culture and differentiation in-vitro. These results indicate that the proposed hybrid systems, combining osteoinductive 3D-printed scaffolds and cell-laden hydrogels, have great potential for bone tissue engineering and are expected to be applied in the treatment of bone defects based on active tissue regeneration.

Testing homogeneity: the trouble with sparse functional data.

Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be done for such data. In particular, we show that testing the marginal homogeneity based on point-wise distributions is feasible under some mild constraints and propose a new two-sample statistic that works well with both intensively and sparsely measured functional data. The proposed test statistic is formulated upon energy distance, and the convergence rate of the test statistic to its population version is derived along with the consistency of the associated permutation test. The aptness of our method is demonstrated on both synthetic and real data sets.

Characteristics of breast cancers detected by screening mammography in Taiwan: a single institute's experience.

BACKGROUND/AIM: Breast cancer is the most common female malignancy in the world. Nearly ninety percent of screening-detected breast cancers were diagnosed with earlier stages of 0 to II in Taiwan. It's widely acknowledged that mammography screening of breast cancer can achieve the goal of early diagnosis and treatment in terms of preventive task while neglected interval cancers are associated with unfavorable tumor characteristics and worse outcomes. The purpose of this study was to explore the characteristics of screening-detected breast cancers in Taiwan. MATERIALS AND METHODS: Both screening and diagnostic mammography examinations with accompanied BI-RADS (breast imaging-reporting and data system) classification were extracted from the health information system and linked to cancer registry in Taiwan. Enrolled population included those attending their first mammography between 2012 and 2016, excluding subjects with previous breast cancer, or with missing or incomplete data. We compared treatment outcomes between breast cancers with either initial screening or diagnostic mammography (control group), and investigated the compositions of breast cancers detected by screening mammography through direct chart reviews. RESULTS: A total of 84,246 screening and 61,230 diagnostic mammography sessions were performed from 2010 to 2020. More positive results (BI-RADS 0, 3, 4 and 5) were observed for those attending the first diagnostic than the first screening mammography (43.58% versus 16.12%, p < 0.001). Earlier stages (0 and I) distribution (92% versus 81%, p < 0.0001), better survivorship (overall survival: 96.91% versus 92.17%, p = 0.007) and a lower HER2 (human epidermal growth factor receptor II) positive status and lower tumor grade were noted in breast cancers with initial screening rather than diagnostic mammography. Among 26,103 mammography screening invitees between 2012 and 2016, 325 breast cancers were ascertained from cancer registry. Of these, 234 had one, 72 had two and 19 had three episodes of mammography before cancer diagnosis. Extensive chart reviews revealed that women with and without breast symptoms constituted 29.9 and 70.1% of the 325 screening-detected breast cancers, with the latter further divided into false negative results (interval cancer), diagnosed at the first mammography, diagnostic at the secondary or subsequent mammography and those with a delayed biopsy or confirmatory imaging constituted (5.2, 47, 10.5 and 7.4%). CONCLUSION: Screening-detected breast cancers were a mixture of women with and without symptoms, those with a false negative result, true negative results with cancer detected at subsequent mammography and non-adherers. Despite this, efficacy of mammography screening was ascertained in Taiwan from this study. To further enhance earlier detection and decrease false negativity, the impact of repeated mammography, and additional sonography for symptomatic women, compliance following a positive screening mammography should not be overemphasized.

A YOLO-based AI system for classifying calcifications on spot magnification mammograms.

OBJECTIVES: Use of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies. METHODS: In this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis. RESULTS: We included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868-0.908), with a sensitivity of 88.4% (95% CI 86.9-8.99%), specificity of 80.8% (95% CI 77.6-84%), and an accuracy of 84.6% (95% CI 81.8-87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided. CONCLUSION: The AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.

Controllable-Swelling Microneedle-Assisted Ultrasensitive Paper Sensing Platforms for Personal Health Monitoring.

Microneedle (MN) patches, which allow the extraction of skin interstitial fluid (ISF) without a pain sensation, are powerful tools for minimally invasive biofluid sampling. Herein, an MN-assisted paper-based sensing platform that enables rapid and painless biofluid analysis with ultrasensitive molecular recognition capacity is developed. First, a controllable-swelling MN patch is constructed through the engineering of a poly(ethylene glycol) diacrylate/methacrylated hyaluronic acid hydrogel; it combines rapid, sufficient extraction of ISF with excellent structural integrity. Notably, the analyte molecules in the needles can be recovered into a moist cellulose paper through spontaneous diffusion. More importantly, the paper can be functionalized with enzymatic colorimetric reagents or a plasmonic array, enabling a desired detection capacity-for example, the use of paper-based surface-enhanced Raman spectroscopy sensors leads to label-free, trace detection (sub-ppb level) of a diverse set of molecules (cefazolin, nicotine, paraquat, methylene blue). Finally, nicotine is selected as a model drug to evaluate the painless monitoring of three human volunteers. The changes in the nicotine levels can be tracked, with the levels varying significantly in response to the metabolism of drug in different volunteers. This as-designed minimally invasive sensing system should open up new opportunities for precision medicine, especially for personal healthcare monitoring.

Trajectories of brain volumes in young children are associated with maternal education.

Brain growth in early childhood is reflected in the evolution of proportional cerebrospinal fluid volumes (pCSF), grey matter (pGM), and white matter (pWM). We study brain development as reflected in the relative fractions of these three tissues for a cohort of 388 children that were longitudinally followed between the ages of 18 and 96 months. We introduce statistical methodology (Riemannian Principal Analysis through Conditional Expectation, RPACE) that addresses major challenges that are of general interest for the analysis of longitudinal neuroimaging data, including the sparsity of the longitudinal observations over time and the compositional structure of the relative brain volumes. Applying the RPACE methodology, we find that longitudinal growth as reflected by tissue composition differs significantly for children of mothers with higher and lower maternal education levels.

Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education.

The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.

Development of 3D-Printed, Biodegradable, Conductive PGSA Composites for Nerve Tissue Regeneration.

Nerve conduits are used to reconnect broken nerve bundles and provide protection to facilitate nerve regeneration. However, the low degradation rate and regeneration rate, as well as the requirement for secondary surgery are some of the most criticized drawbacks of existing nerve conduits. With high processing flexibility from the photo-curability, poly (glycerol sebacate) acrylate (PGSA) is a promising material with tunable mechanical properties and biocompatibility for the development of medical devices. Here, polyvinylpyrrolidone (PVP), silver nanoparticles (AgNPs), and graphene are embedded in biodegradable PGSA matrix. The polymer composites are then assessed for their electrical conductivity, biodegradability, three-dimensional-printability (3D-printability), and promotion of cell proliferation. Through the four-probe technique, it is shown that the PGSA composites are identified as highly conductive in swollen state. Furthermore, biodegradability is evaluated through enzymatic degradation and facilitated hydrolysis. Cell proliferation and guidance are significantly promoted by three-dimensional-printed microstructures and electrical stimulation on PGSA composites, especially on PGSA-PVP. Hence, microstructured nerve conduits are 3D-printed with PGSA-PVP. Guided cell growth and promoted proliferation are subsequently demonstrated by Schwann cell culture combined with electrical stimulation. Consequently, 3D-printed nerve conduits fabricated with PGSA composites hold great potential in nerve tissue regeneration through electrical stimulation.

Demystifying BRAF Mutation Status in Colorectal Liver Metastases : A Multi-institutional, Collaborative Approach to 6 Open Clinical Questions.

OBJECTIVE: To investigate the clinical implications of BRAF -mutated (mut BRAF ) colorectal liver metastases (CRLMs). BACKGROUND: The clinical implications of mut BRAF status in CRLMs are largely unknown. METHODS: Patients undergoing resection for mut BRAF CRLM were identified from prospectively maintained registries of the collaborating institutions. Overall survival (OS) and recurrence-free survival (RFS) were compared among patients with V600E versus non-V600E mutations, KRAS/BRAF comutation versus mut BRAF alone, microsatellite stability status (Microsatellite Stable (MSS) vs instable (MSI-high)), upfront resectable versus converted tumors, extrahepatic versus liver-limited disease, and intrahepatic recurrence treated with repeat hepatectomy versus nonoperative management. RESULTS: A total of 240 patients harboring BRAF -mutated tumors were included. BRAF V600E mutation was associated with shorter OS (30.6 vs 144 mo, P =0.004), but not RFS compared with non-V600E mutations. KRAS/BRAF comutation did not affect outcomes. MSS tumors were associated with shorter RFS (9.1 vs 26 mo, P <0.001) but not OS (33.5 vs 41 mo, P =0.3) compared with MSI-high tumors, whereas patients with resected converted disease had slightly worse RFS (8 vs 11 mo, P =0.01) and similar OS (30 vs 40 mo, P =0.4) compared with those with upfront resectable disease. Patients with extrahepatic disease had worse OS compared with those with liver-limited disease (8.8 vs 40 mo, P <0.001). Repeat hepatectomy after intrahepatic recurrence was associated with improved OS compared with nonoperative management (41 vs 18.7 mo, P =0.004). All results continued to hold true in the multivariable OS analysis. CONCLUSIONS: Although surgery may be futile in patients with BRAF -mutated CRLM and concurrent extrahepatic disease, resection of converted disease resulted in encouraging survival in the absence of extrahepatic spread. Importantly, second hepatectomy in select patients with recurrence was associated with improved outcomes. Finally, MSI-high status identifies a better prognostic group, with regard to RFS while patients with non-V600E mutations have excellent prognosis.

Performance of the 7 th and 8 th Editions of the American Joint Committee on Cancer Staging System in Patients with Intraductal Papillary Mucinous Neoplasm-Associated PDAC : A Multi-institutional Analysis.

OBJECTIVE: To validate the 7 th and 8 th editions of the AJCC staging system for patients with invasive carcinomas arising in association with IPMN (IPMN-associated PDAC). BACKGROUND DATA: Although several studies have validated AJCC systems in patients with conventional PDAC, their applicability to IPMN-associated PDAC has not been assessed. METHODS: Two hundred seventy-five patients who underwent resection for IPMN-associated PDAC between 1996 and 2015 at 3 tertiary centers and had data on the size of the invasive component and lymph node status were identified. Concordance probability estimates (CPE) were calculated and recursive partitioning analysis was employed to identify optimal prognostic cutoffs for T and N. RESULTS: The CPE for the 7 th and 8 th editions of the AJCC schema were relatively good (0.64 for both) and similar for colloid and tubular subtypes (0.64 for both). The 8 th edition introduced T1a sub-staging and a new distinction between N1 and N2. The utility of the former was confirmed, although the latter did not improve prognostic discrimination. The successful validation of the 8th edition of the AJCC criteria in patients with tubular and colloid subtypes allowed us to compare these patients in early vs late T and N stages which showed that with advanced disease, the prognostic superiority of colloid tumors over their tubular counterparts diminishes. CONCLUSIONS: Our findings support the use of the AJCC 8 th edition in the IPMN-associated PDAC population, but suggest that certain cutoffs may need to be revisited. In advanced AJCC stages, patients with colloid vs tubular subtypes have comparable prognosis.