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Radiology Radiology

Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram.

In Academic radiology

RATIONALE AND OBJECTIVES : Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC.

MATERIALS AND METHODS : Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup.

RESULTS : The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001).

CONCLUSION : Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.

Lin Mengyan, Lin Naier, Yu Sihui, Sha Yan, Zeng Yan, Liu Aie, Niu Yue

2023-Mar-14

Deep learning, Early Recurrence, Multiparametric magnetic resonance imaging, Radiomics, Sinonasal, Squamous cell carcinoma

General General

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.

In Analytica chimica acta

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Fang Wei, Du Zhuokun, Kong Linlin, Fu Bin, Wang Guibin, Zhang Yangjun, Qin Weijie

2023-Apr-22

Cellular heterogeneity, MS1-only acquisition, Retention time prediction, Single-cell proteomics

General General

A classification model for detection of ductal carcinoma in situ by Fourier transform infrared spectroscopy based on deep structured semantic model.

In Analytica chimica acta

At present, deep learning is widely used in spectral data processing. Deep learning requires a large amount of data for training, while the collection of biological serum spectra is limited by sample numbers and labor costs, so it is impractical to obtain a large amount of serum spectral data for disease detection. In this study, we propose a spectral classification model based on the deep structured semantic model (DSSM) and successfully apply it to Fourier Transform Infrared (FT-IR) spectroscopy for ductal carcinoma in situ (DCIS) detection. Compared with the traditional deep learning model, we match the spectral data into positive and negative pairs according to whether the spectra are from the same category. The DSSM structure is constructed by extracting features according to the spectral similarity of spectra pairs. This new construction model increases the data amount used for model training and reduces the dimension of spectral data. Firstly, the FT-IR spectra are paired. The spectra pairs are labeled as positive pairs if they come from the same category, and the spectra pairs are labeled as negative pairs if they come from different categories. Secondly, two spectra in each spectra pair are put into two deep neural networks of the DSSM structure separately. Then the spectral similarity between the output feature maps of two deep neural networks is calculated. The DSSM structure is trained by maximizing the conditional likelihood of the spectra pairs from the same category. Thirdly, after the training of DSSM is done, the training set and testing set are input into two deep neural networks separately. The output feature maps of the training set are put into the reference library. Lastly, the k-nearest neighbor (KNN) model is used for classification according to Euclidean distances between the output feature map of each unknown sample to the reference library. The category of the unknown sample is judged according to the categories of k nearest samples. We also use principal component analysis (PCA) to reduce dimension for comparison. The accuracies of the KNN model, principal component analysis-k nearest neighbor (PCA-KNN) model, and deep structured semantic model-k nearest neighbor (DSSM-KNN) model are 78.8%, 72.7%, and 97.0%, which proves that our proposed model has higher accuracy.

Du Yu, Xie Fei, Wu Guohua, Chen Peng, Yang Yang, Yang Liu, Yin Longfei, Wang Shu

2023-Apr-22

Deep structured semantic model, Detection, Ductal carcinoma in situ, Fourier Transform Infrared spectroscopy

Public Health Public Health

Convolutional Neural Network Quantification of Gleason Pattern 4 and Association with Biochemical Recurrence in Intermediate Grade Prostate Tumors.

In Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Differential classification of prostate cancer (CaP) grade group (GG) 2 and 3 tumors remains challenging, likely due to the subjective quantification of percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-assessed %GP4 is associated with biochemical recurrence (BCR) risk in intermediate risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999-2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate four tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n=45) and 4 (n=20) tumor foci. The CNN model was applied to GG 2 (n=153) and 3 (n=62) for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the four tissue types. Further, CNN-assessed %GP4 was significantly higher in GG 3 compared with GG 2 tumors (p=7.2*10-11). %GP4 was associated with an increased risk of BCR (adjusted HR=1.09 per 10% increase in %GP4, p=0.010) in GG 2 and 3 tumors. Within GG 2 tumors specifically, %GP4 was more strongly associated with BCR (adjusted HR=1.12, p=0.006). Our findings demonstrate the feasibility of CNN-assessed %GP4 estimation, which is associated with BCR risk. This objective approach could be added to the standard pathological assessment for patients with GG 2 and 3 tumors and act as a surrogate for specialist genitourinary pathologist evaluation when such consultation is not available.

Chen Yalei, Loveless Ian M, Nakai Tiffany, Newaz Rehnuma, Abdollah Firas F, Rogers Craig G, Hassan Oudai, Chitale Dhananjay, Arora Kanika, Williamson Sean R, Gupta Nilesh S, Rybicki Benjamin A, Sadasivan Sudha M, Levin Albert M

2023-Mar-14

General General

Kobayashi Award 2021: Neuropeptides, receptors, and follicle development in the ascidian, Ciona intestinalis Type A: new clues to the evolution of chordate neuropeptidergic systems from biological niches.

In General and comparative endocrinology

Ciona intestinalis Type A (Ciona robusta) is a cosmopolitan species belonging to the phylum Urochordata, invertebrate chordates that are phylogenetically the most closely related to the vertebrates. Therefore, this species is of interest for investigation of the evolution and comparative physiology of endocrine, neuroendocrine, and nervous systems in chordates. Our group has identified more than 30 Ciona neuropeptides (80% of all identified ascidian neuropeptides) praimarily using peptidomic approaches combined with reference to genome sequences. These neuropeptides are classified into two groups: homologs or prototypes of vertebrate neuropeptides and novel (Ciona-specific) neuropeptides. We have also identified the cognate receptors for these peptides. In particular, we elucidated multiple receptors for Ciona-specific neuropeptides by a combination of a novel machine learning system and experimental validation of the specific interaction of the predicted neuropeptide-receptor pairs, and verified unprecedented phylogenies of receptors for neuropeptides. Moreover, several neuropeptides were found to play major roles in the regulation of ovarian follicle development. Ciona tachykinin facilitates the growth of vitellogenic follicles via up-regulation of the enzymatic activities of proteases. Ciona vasopressin stimulates oocyte maturation and ovulation via up-regulation of maturation-promoting factor- and matrix metalloproteinase-directed collagen degradation, respectively. Ciona cholecystokinin also triggers ovulation via up-regulation of receptor tyrosine kinase signaling and the subsequent activation of matrix metalloproteinase. These studies revealed that the neuropeptidergic system plays major roles in ovarian follicle growth, maturation, and ovulation in Ciona, thus paving the way for investigation of the biological roles for neuropeptides in the endocrine, neuroendocrine, nervous systems of Ciona, and studies of the evolutionary processes of various neuropeptidergic systems in chordates.

Satake Honoo

2023-Mar-14

Ciona intestinalis Type A, ascidian, follicle, neuropeptide, peptide hormone, receptor

Internal Medicine Internal Medicine

The use of Smart Environments and Robots for Infection Prevention Control: a systematic literature review.

In American journal of infection control ; h5-index 43.0

Infection prevention and control (IPC) is essential to prevent nosocomial infections. The implementation of automation technologies can aid outbreak response. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings. The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The quality of the included studies was appraised by two authors. The search strategy returned 1520 citations and 17 papers were included in this review. This review identified three main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (i.e., air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of health care workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Piaggio Davide, Zarro Marianna, Pagliara Silvio, Andellini Martina, Almuhini Abdulaziz, Maccaro Alessia, Pecchia Leandro

2023-Mar-14

Infection prevention and control, artificial intelligence, hand hygiene, health 4.0, internet of things, robot