Advanced low grade serous ovarian cancer: A retrospective analysis of surgical and chemotherapeutic management in two high volume oncological centers.

Simple summary: Low-grade serous ovarian cancer (LGSOC) represents an uncommon histotype of serous ovarian cancer (accounting for approximately 5% of all ovarian cancer) with a distinct behavior compared to its high-grade serous counterpart, characterized by a better prognosis and low response rate to chemotherapeutic agents. Similar to high-grade serous ovarian cancer, cytoreductive surgery is considered crucial for patient survival. This retrospective study aimed to analyze the outcomes of women affected by advanced stages (III-IV FIGO) of LGSOC from two high-volume oncological centers for ovarian neoplasm. In particular, we sought to evaluate the impact on survival outcomes of optimal cytoreductive surgery [i.e., residual disease (RD) <10 mm at the end of surgery]. The results of our work confirm the role of complete cytoreduction (i.e., no evidence of disease after surgery) in the survival of patients and even the positive prognostic role of a minimal RD (i.e., <10 mm), whenever complete cytoreduction cannot be achieved. Background: Low-grade serous ovarian cancer (LGSOC) is a rare entity with different behavior compared to high-grade serous (HGSOC). Because of its general low chemosensitivity, complete cytoreductive surgery with no residual disease is crucial in advanced stage LGSOC. We evaluated the impact of optimal cytoreduction on survival outcome both at first diagnosis and at recurrence. Methods: We retrospectively studied consecutive patients diagnosed with advanced LGSOCs who underwent cytoreductive surgery in two oncological centers from January 1994 to December 2018. Survival curves were estimated by the Kaplan-Meier method, and 95% confidence intervals (95% CI) were estimated using the Greenwood formula. Results: A total of 92 patients were included (median age was 47 years, IQR 35-64). The median overall survival (OS) was 142.3 months in patients with no residual disease (RD), 86.4 months for RD 1-10 mm and 35.2 months for RD >10 mm (p = 0.002). Progression-free survival (PFS) was inversely related to RD after primary cytoreductive surgery (RD = 0 vs RD = 1-10 mm vs RD >10 mm, p = 0.002). On multivariate analysis, RD 1-10 mm (HR = 2.30, 95% CI 1.30-4.06, p = 0.004), RD >10 mm (HR = 3.89, 95% CI 1.92-7.88, p = 0.0004), FIGO stage IV (p = 0.001), and neoadjuvant chemotherapy (NACT) (p = 0.010) were independent predictors of PFS. RD >10 mm (HR = 3.13, 95% CI 1.52-6.46, p = 0.004), FIGO stage IV (p <0.0001) and NACT (p = 0.030) were significantly associated with a lower OS. Conclusions: Optimal cytoreductive surgery improves survival outcomes in advanced stage LGSOC s . When complete debulking is impossible, a RD <10 mm confers better OS compared to an RD >10 mm in this setting of patients.

A Novel GC-FID Analytical Strategy Reveals Adulteration of Fuels by Vegetable Oil.

BACKGROUND: Adulteration of diesel fuel by the addition of vegetable oil is a problem that touches several countries around the world and bypasses the complexity of the specifications regarding the automotive diesel fuel distinguishing between customs, fiscal, and commercial-environmental specifications. At an international level, the adoption of the same analysis methods is important for the harmonization processes and the fluidity of the market. In this context, we assist to the diffusion of the same fraud touching several countries or continents since the limit of the same methods are common to many specifications. For several European countries, the revenue lost as a result of this adulteration consists of billions of euros per year. This enormous amount of illicit money feeds organized criminal networks with huge social and environmental damages. OBJECTIVE: This work presents a GC method to quantify vegetable oils in the range of 0.2-7% (w/w) in adulterated diesel fuel, intended for use as motor fuel, with or without extraneous heavy mineral oil. METHODS: Our study was realized on 100 fraudulent samples collected by the Italian fiscal police in regular oil stations and by the Italian fiscal police and customs officers as a consequence of controls on trucks transporting, in suspicious circumstances, "oil" often declared "antistick and anticorrosion oil" or "protective oil." Conclusions and Highlights: High reliability of results, external validity, ease of replication, simple instrumentation, and sample preparation make this method well suited for a new "smart" protocol for diesel fuels analysis for customs, fiscal, and antifraud purposes.

Stochastic Training of Neural Networks via Successive Convex Approximations.

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN's weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.

Indocyanine Green versus Radiotracer with or without Blue Dye for Sentinel Lymph Node Mapping in Stage >IB1 Cervical Cancer (>2 cm).

STUDY OBJECTIVE: To compare sentinel lymph node (SLN) mapping in women with cervical cancer stage >IB1 (tumor size >2 cm) using indocyanine green (ICG) versus the standard technique using radioisotope technetium 99m radiocolloid (Tc99m) radiotracer with or without blue dye. DESIGN: European multicenter, retrospective observational study (Canadian Task Force classification II-2). SETTING: Four academic medical centers. PATIENTS: Ninety-five women with stage IB1 cervical cancer (>2 cm) who underwent SLN mapping with Tc99m with or without blue dye or ICG and radical hysterectomy. INTERVENTION: The detection rate and bilateral mapping rate were compared between ICG and standard Tc99m radiotracer with or without blue dye. Lymphadenectomy was performed, and the false-negative rate was assessed. MEASUREMENTS AND MAIN RESULTS: Forty-seven patients underwent SLN mapping with Tc99m with or without blue dye, and 48 did so with ICG. All patients underwent radical hysterectomy with or without bilateral salpingo-oophorectomy between 2008 and 2016. The overall detection rate of SLN mapping was 91.5% for Tc99m with or without blue dye and 100% for ICG. A 91.7% rate of bilateral migration was achieved for ICG, significantly higher than the 66% obtained with Tc99m with or without blue dye (p = .025). Nine of the 23 SLN-positive patients (39.1%) were diagnosed exclusively as a result of the ultrastaging used to identify micrometastases or isolated tumor cells only. CONCLUSIONS: In advanced cervical cancer (stage IB1 >2 cm), the detection rate and bilateral migration rate on real-time fluorescent SLN mapping were higher with ICG than with Tc99m radiotracer with or without blue dye. SLN mapping and ultrastaging can provide additional information for nodal staging in advanced cervical cancer. In this setting, ICG is a promising tool for mapping, appearing less affected by higher disease stage compared with traditional methods.

A framework for parallel and distributed training of neural networks.

The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where training data is partitioned over a set of agents that communicate with each other through a sparse, possibly time-varying, connectivity pattern. In such distributed scenario, the training problem can be formulated as the (regularized) optimization of a non-convex social cost function, given by the sum of local (non-convex) costs, where each agent contributes with a single error term defined with respect to its local dataset. To devise a flexible and efficient solution, we customize a recently proposed framework for non-convex optimization over networks, which hinges on a (primal) convexification-decomposition technique to handle non-convexity, and a dynamic consensus procedure to diffuse information among the agents. Several typical choices for the training criterion (e.g., squared loss, cross entropy, etc.) and regularization (e.g., â„“2 norm, sparsity inducing penalties, etc.) are included in the framework and explored along the paper. Convergence to a stationary solution of the social non-convex problem is guaranteed under mild assumptions. Additionally, we show a principled way allowing each agent to exploit a possible multi-core architecture (e.g., a local cloud) in order to parallelize its local optimization step, resulting in strategies that are both distributed (across the agents) and parallel (inside each agent) in nature. A comprehensive set of experimental results validate the proposed approach.

Distributed semi-supervised support vector machines.

The semi-supervised support vector machine (S(3)VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S(3)VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over networks, where communication is restricted only to neighboring agents and no coordinating authority is present. Using a standard relaxation of the original S(3)VM, we formulate the training problem as the distributed minimization of a non-convex social cost function. To find a (stationary) solution in a distributed manner, we employ two different strategies: (i) a distributed gradient descent algorithm; (ii) a recently developed framework for In-Network Nonconvex Optimization (NEXT), which is based on successive convexifications of the original problem, interleaved by state diffusion steps. Our experimental results show that the proposed distributed algorithms have comparable performance with respect to a centralized implementation, while highlighting the pros and cons of the proposed solutions. To the date, this is the first work that paves the way toward the broad field of distributed semi-supervised learning over networks.

Sentinel Lymph Node Mapping With Near-Infrared Fluorescent Imaging Using Indocyanine Green: A New Tool for Laparoscopic Platform in Patients With Endometrial and Cervical Cancer.

Indocyanine green (ICG) represents a feasible alternative to the more traditional methods of sentinel lymph node (SLN) mapping, and interest in this promising tracer is growing. This report outlines our experience with ICG in a minimally invasive laparoscopic approach in women with endometrial cancer and cervical cancer using the Storz SPIES ICG near-infrared fluorescence imaging technology. A total of 49 patients with clinical stage I endometrial cancer (n = 40) or stage I cervical cancer (n = 9) were retrospectively reviewed. All patients had undergone simple or radical laparoscopic hysterectomy with pelvic and/or aortic lymphadenectomy and SLN mapping by means of an intracervical injection of ICG dye at the 3 o'clock and 9 o'clock locations after the induction of general anesthesia. The detection rate of ICG was 100% (49 of 49). The rate of bilateral SLN detection was 86% (42 of 49). Positive lymph nodes were found in 6 patients (12%), with at least 1 positive SLN. The sensitivity and negative predictive value of SLN detection were 100%. All procedures were successfully completed without conversion to open laparotomy, and no intraoperative or postoperative complications occurred. In our preliminary experience, ICG showed a high overall detection rate, and bilateral mapping appears to be a feasible alternative to the more traditional methods of SLN mapping in patients with endometrial cancer and cervical cancer. Laparoscopic SLN mapping with ICG appears to be safe, easy, and reproducible, with a positive impact on patient management.

Synthesis of poly(aryleneethynylene)s bearing glucose units as substituents.

A series of poly(aryleneethynylene)s functionalized with acetylated glucopyranosyl units were synthesized by the Pd-catalyzed reaction of trimethylsilylethynyl derivatives with aromatic halides in the presence of silver oxide.