Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort

Importance: Ultrasonography-based risk models can help nonexpert clinicians evaluate adnexal lesions and reduce surgical interventions for benign tumors. Yet, these models have limited uptake in the US, and studies comparing their diagnostic accuracy are lacking. Objective: To evaluate, in a US cohort, the diagnostic performance of 3 ultrasonography-based risk models for differentiating between benign and malignant adnexal lesions: International Ovarian Tumor Analysis (IOTA) Simple Rules with inconclusive cases reclassified as malignant or reevaluated by an expert, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX), and Ovarian-Adnexal Reporting and Data System (O-RADS). Design, setting, and participants: This retrospective diagnostic study was conducted at a single US academic medical center and included consecutive patients aged 18 to 89 years with adnexal masses that were managed surgically or conservatively between January 2017 and October 2022. Exposure: Evaluation of adnexal lesions using the Simple Rules, ADNEX, and O-RADS. Main outcomes and measures: The main outcome was diagnostic performance, including area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Surgery or follow-up were reference standards. Secondary analyses evaluated the models' performances stratified by menopause status and race. Results: The cohort included 511 female patients with a 15.9% malignant tumor prevalence (81 patients). Mean (SD) ages of patients with benign and malignant adnexal lesions were 44.1 (14.4) and 52.5 (15.2) years, respectively, and 200 (39.1%) were postmenopausal. In the ROC analysis, the AUCs for discriminative performance of the ADNEX and O-RADS models were 0.96 (95% CI, 0.93-0.98) and 0.92 (95% CI, 0.90-0.95), respectively. After converting the ADNEX continuous individualized risk into the discrete ordinal categories of O-RADS, the ADNEX performance was reduced to an AUC of 0.93 (95% CI, 0.90-0.96), which was similar to that for O-RADS. The Simple Rules combined with expert reevaluation had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 91.9% specificity (95% CI, 88.9%-94.3%), and the Simple Rules combined with malignant classification had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 88.1% specificity (95% CI, 84.7%-91.0%). At a 10% risk threshold, ADNEX had 91.4% sensitivity (95% CI, 83.0%-96.5%) and 86.3% specificity (95% CI, 82.7%-89.4%) and O-RADS had 98.8% sensitivity (95% CI, 93.3%-100%) and 74.4% specificity (95% CI, 70.0%-78.5%). The specificities of all models were significantly lower in the postmenopausal group. Subgroup analysis revealed high performances independent of race. Conclusions and relevance: In this diagnostic study of a US cohort, the Simple Rules, ADNEX, and O-RADS models performed well in differentiating between benign and malignant adnexal lesions; this outcome has been previously reported primarily in European populations. Risk stratification models can lead to more accurate and consistent evaluations of adnexal masses, especially when used by nonexpert clinicians, and may reduce unnecessary surgeries.</p

A streamlined mass spectrometry-based proteomics workflow for large scale FFPE tissue analysis

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner

Deep Visual Proteomics defines single-cell identity and heterogeneity

Peer reviewe

Superconductivity in Ce- and U-based "122" heavy-fermion compounds

This review discusses the heavy-fermion superconductivity in Ce- and U-based compounds crystallizing in the body-centered tetragonal ThCr2Si2 structure. Special attention will be paid to the theoretical background of these systems which are located close to a magnetic instability.Comment: 12 pages, 9 figures. Invited topical review (special issue on "Recent Developments in Superconductivity") Metadata and references update

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference

Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons

The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies

Deconstructing tumor heterogeneity: the stromal perspective.

Significant advances have been made towards understanding the role of immune cell-tumor interplay in either suppressing or promoting tumor growth, progression, and recurrence, however, the roles of additional stromal elements, cell types and/or cell states remain ill-defined. The overarching goal of this NCI-sponsored workshop was to highlight and integrate the critical functions of non-immune stromal components in regulating tumor heterogeneity and its impact on tumor initiation, progression, and resistance to therapy. The workshop explored the opposing roles of tumor supportive versus suppressive stroma and how cellular composition and function may be altered during disease progression. It also highlighted microenvironment-centered mechanisms dictating indolence or aggressiveness of early lesions and how spatial geography impacts stromal attributes and function. The prognostic and therapeutic implications as well as potential vulnerabilities within the heterogeneous tumor microenvironment were also discussed. These broad topics were included in this workshop as an effort to identify current challenges and knowledge gaps in the field

Are We Ready for Hyperthermic Intraperitoneal Chemotherapy in the Upfront Treatment of Ovarian Cancer?

No abstrac

An Orally Available Small-Molecule Inhibitor of c-Met, PF-2341066, Reduces Tumor Burden and Metastasis in a Preclinical Model of Ovarian Cancer Metastasis1

Deregulated expression of the hepatocyte growth factor (HGF) receptor, c-Met, in cancer contributes to tumor progression and metastasis. The objective of this study was to determine whether blocking c-Met with an orally available c-Met inhibitor, PF-2341066, reduces tumor burden and increases survival in a xenograft model of ovarian cancer metastasis. Treatment of mice injected interperitoneally with SKOV3ip1 cells showed reduced overall tumor burden. Tumor weight and the number of metastases were reduced by 55% (P < .0005) and 62% (P < .0001), respectively. Treatment also increased median survival from 45 to 62 days (P = .0003). In vitro, PF-2341066 reduced HGF-stimulated phosphorylation of c-Met in the tyrosine kinase domain as well as phosphorylation of the downstream signaling effectors, Akt and Erk. It was apparent that inhibition of the pathways was functionally important because HGF-induced branching morphogenesis was also inhibited. In addition, proliferation and adhesion to various extracellular matrices were inhibited by treatment with PF-2341066, and the activity of matrix metalloproteinases was decreased in tumor tissue from treated mice compared with those receiving vehicle. Overall, these data indicate that PF-2341066 effectively reduces tumor burden in an in vivo model of ovarian cancer metastasis and may be a good therapeutic candidate in the treatment of patients with ovarian cancer