LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning

Most existing Legal Judgment Prediction (LJP) models focus on discovering the legal triggers in the criminal fact description. However, in real-world scenarios, a professional judge not only needs to assimilate the law case experience that thrives on past sentenced legal judgments but also depends on the professional legal grounded reasoning that learned from professional legal knowledge. In this paper, we propose a LegalDuet model, which pretrains language models to learn a tailored embedding space for making legal judgments. It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions. Our experiments show that LegalDuet achieves state-of-the-art performance on the CAIL2018 dataset and outperforms baselines with about 4% improvements on average. Our dual-view reasoning based pretraining can capture critical legal clues to learn a tailored embedding space to distinguish criminal cases. It reduces LegalDuet's uncertainty during prediction and brings pretraining advances to the confusing/low frequent charges. All codes are available at https://github.com/NEUIR/LegalDuet.Comment: we will update this paper and revise this paper in the near futur

Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography

Abstract Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future

ETCTN/NCI 10330: A phase 2 study of belinostat with SGI-110 (guadecitabine) or ASTX727 (decitabine/cedazuridine) for the treatment of unresectable and metastatic conventional chondrosarcoma

11531 Background: Conventional chondrosarcoma (cCS) is the 2nd most common primary bone tumor and is resistant to chemotherapy and radiation. IDH1/2 mutations (m) occur in 50% of cCS. Both IDHm and wild-type (wt) cCS harbor epigenetic dysregulation. In preclinical models of IDHm and wt cCS, combination treatment with HDAC and DNMT inhibitors (i) suppressed growth in vitro and in vivo by reversing the hypermethylated state and inducing tumor suppressors, interferon response genes and apoptosis (Sheikh T, Schwartz G. Mol Cancer Ther 2021;20). Methods: NCI 10330 is a single-arm, multicenter, phase 2 study evaluating the HDACi belinostat (B) with the DNMTi SGI-110 (S) or ASTX727 (A). A replaced S due to drug availability (pts were replaced). Pts had advanced cCS, ECOG PS ≤ 2 and could be treatment naïve. Progression was required for grade 1 cCS. Pts received B 1000mg/m2 IV + S 45mg/m2 SC both days 1-5 or B (same dosing) + A (cedazuridine 100mg/decitabine 35mg) PO both days 1-5, in 28-day cycles. 1° endpoint was objective response. A Simon 2-stage design was used. If ≥ 2/13 responses occurred in stage 1, the study would proceed to full accrual. Design had 85% power with α = 0.05 to test ORR 8% vs 28%. 2° endpoints included safety, PFS and OS. A safety lead-in was performed. Paired biopsies were collected. Results: Stage 1 is complete. 19 pts were treated: 6 on B+S and 13 on B+A. Median age was 50 and 67 years, respectively. All pts had prior surgery. 17% (B+S) and 38% (B+A) had prior radiation. 33% (B+S) and 55% (B+A) were IDHm. 67% (B+S) and 75% (B+A) were histologic grade ≥ 2. There were no objective responses. Best response (at 8 weeks) was stable disease (SD) in 4/6 pts (67%) on B+S and 6/10 pts (60%) on B+A. mPFS was 4.2 mos (95% CI 1.97-NR) for B+S and 3.8 mos (95% CI 2.17-NR) for B+A. mOS has not been reached. For B+A, mPFS for IDHm vs wt pts was 4.7 and 3.1 mos, respectively (p=0.21). One pt with IDHm grade 2 cCS who progressed on FT-2102 (IDH1i) remains on B+A > 1 year. There were no DLTs during either safety lead-in. Grade 3/4 treatment-related adverse events (TRAEs) occurred in 17% (B+S) and 69% (B+A). For B+A, the most common grade 3/4 TRAE was neutropenia (54%) and the most common all-grade TRAEs were nausea (69%), leukopenia (61%), neutropenia (54%), anemia (46%) and fatigue (46%). Paired tumor biopsies are being evaluated with whole exome sequencing, RNAseq, methylation array and multiplex IHC with results forthcoming. Conclusions: Combination HDACi + DNMTi was well-tolerated in advanced cCS. There were no objective responses; however, a subset of pts experienced prolonged SD with a trend towards improved mPFS in IDHm pts. Correlative work is ongoing with a focus on differential effects on IDHm tumors and whether modulation of the immune microenvironment might support combinations with immunotherapy. Support: UM1CA186689. Clinical trial information: NCT04340843