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  1. AU="Guoquan Cao"
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  3. AU="Phelan, Stefan"
  4. AU=Jensen Eric Allen
  5. AU=Li Jing
  6. AU="Barroso, Blanca"
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  15. AU="Svaasand, Eva K"
  16. AU="Cox, Felicity"
  17. AU="David, Nathaniel E"
  18. AU=Rammes G
  19. AU="Esbenshade, Tim"
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  35. AU="Resnick, Adam C"
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  40. AU="Serwin, Natalia Maria"
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  42. AU="Yin-Yin Xie"
  43. AU=White David P
  44. AU="Maria Teresa Viadero"
  45. AU="Wingeter, Márcia A"
  46. AU="Stein, Joshua D"
  47. AU="De Vecchis, Liana"
  48. AU="Chapman, Janet"
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  1. Article ; Online: Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images

    Xiaoli Wu / Hao Wu / Shouliang Miao / Guoquan Cao / Huang Su / Jie Pan / Yilun Xu

    BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-

    a binary classification approach

    2024  Volume 9

    Abstract: Abstract Background Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. ... ...

    Abstract Abstract Background Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives. Method We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation. Results Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability. Conclusion ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.
    Keywords Esophageal squamous cell carcinoma ; Deep learning ; Computed tomography ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer

    Shuangquan Li / Yiran Wang / Caiyun Wen / Mingxi Zhu / Meihao Wang / Guoquan Cao

    Frontiers in Cell and Developmental Biology, Vol

    2022  Volume 10

    Abstract: Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising ... ...

    Abstract Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown. In this study, we performed the genome-wide investigation of lncRNA-associated plasma cfDNA 5hmC changes in PAAD by plotting 5hmC reads against lncRNA genes, and identified six PAAD-specific lncRNAs with abnormal 5hmC modifications compared with healthy individuals. Then we applied machine-learning and Cox regression approaches to develop predictive diagnostic (5hLRS) and prognostic (5hLPS) models using the 5hmC-modified lncRNAs. The 5hLRS demonstrated excellent performance in discriminating PAAD from healthy controls with an area under the curve (AUC) of 0.833 in the training cohort and 0.719 in the independent testing cohort. The 5hLPS also effectively divides PAAD patients into high-risk and low-risk groups with significantly different clinical outcomes in the training cohort (log-rank test p = 0.04) and independent testing cohort (log-rank test p = 0.0035). Functional analysis based on competitive endogenous RNA (ceRNA) and enrichment analysis suggested that these differentially regulated 5hmC modified lncRNAs were associated with angiogenesis, circulatory system process, leukocyte differentiation and metal ion homeostasis that are known important events in the development and progression of PAAD. In conclusion, our study indicated the potential clinical utility of 5hmC profiles at lncRNA loci as valuable biomarkers for non-invasive diagnosis and prognostication of cancers.
    Keywords 5-hydroxymethylcytosine ; pancreatic cancer ; machining learning ; long non-coding RNA ; non-invasive biomarker ; Biology (General) ; QH301-705.5
    Subject code 610
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks

    Xiao Chen / Qingshan Deng / Qiang Wang / Xinmiao Liu / Lei Chen / Jinjin Liu / Shuangquan Li / Meihao Wang / Guoquan Cao

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and ... ...

    Abstract PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and MethodsA dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.ResultsThe dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971–0.990 (mean 0.98 ± 0.10), 0.714–0.933 (mean 0.86 ± 0.13), and 0.995–1.000 (mean 0.99 ± 0.12) for the three positions, respectively.ConclusionThis deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.
    Keywords deep learning ; quality control ; U-net ; medical imaging ; radiography ; image segmentation ; Public aspects of medicine ; RA1-1270
    Subject code 006
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Reduced artifacts and improved diagnostic value of 640-slice computed tomography in patients with cardiac pacemakers

    Guoquan Cao / Weijian Chen / Kehua Pan / Houchang Sun / Zhen Wang

    Journal of International Medical Research, Vol

    2019  Volume 47

    Abstract: Objective The aim of this study was to compare the feasibility of 640-slice with 64-slice computed tomography (CT) coronary angiography for diagnosing coronary lesions in patients with pacemakers. Methods Forty-five and 50 patients with pacemakers and ... ...

    Abstract Objective The aim of this study was to compare the feasibility of 640-slice with 64-slice computed tomography (CT) coronary angiography for diagnosing coronary lesions in patients with pacemakers. Methods Forty-five and 50 patients with pacemakers and with suspected or known coronary artery disease underwent 64-slice (64 group) and 640-slice (640 group) CT scans, respectively. All segments of the vessels were evaluated according to the 15-segment model recommended by the American Heart Association. Results The incidence of moderate or severe artifacts was significantly lower (7.27% vs. 32.17%) and the diagnosable rate for coronary lesions was higher (98.91% vs. 94.19%) in the 640 compared with the 64 group. In the 64 group, the incidence of artifacts in patients with a heart rate >65 bpm (20.98%) was higher than in those with a heart rate <65 bpm (15.67%), although the difference was not significant, while the incidence of artifacts was significantly higher in patients with heart arrhythmia (21.40%) compared with in those with normal heart rhythm (15.09%). Conclusions Among patients with pacemakers and a higher heart rate or heart arrhythmia, 640-slice CT may be more effective than 64-slice CT for diagnosing coronary lesions, by reducing moderate and severe artifacts.
    Keywords Medicine (General) ; R5-920
    Subject code 610
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher SAGE Publishing
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images

    Xiao Chen / Yang Zhang / Guoquan Cao / Jiahuan Zhou / Ya Lin / Boyang Chen / Ke Nie / Gangze Fu / Min-Ying Su / Meihao Wang

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: PurposeTo evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.Materials and methodsA total of 48 patients, 28 males and 20 ... ...

    Abstract PurposeTo evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.Materials and methodsA total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21–93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again.ResultsThe median (interquartile range) lesion volume (cm3) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline.ConclusionThe results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.
    Keywords COVID-19 ; computed tomography ; dynamic changes ; registration ; pneumonia ; Public aspects of medicine ; RA1-1270
    Subject code 616
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Surgical resection of pulmonary metastases from colorectal cancer

    Guoquan Cao / Dezhi Cheng / Lechi Ye / Yiyuan Pan / Fan Yang / Shixu Lyu

    PLoS ONE, Vol 12, Iss 4, p e

    11 years of experiences.

    2017  Volume 0175284

    Abstract: To analyze the benefits and prognostic factors after surgical resection of pulmonary metastases from colorectal cancer (CRC).From Jan. 2004 to Jan. 2015, continuous 88 cases diagnosed with pulmonary metastases from CRC, including 15 cases of synchronous ... ...

    Abstract To analyze the benefits and prognostic factors after surgical resection of pulmonary metastases from colorectal cancer (CRC).From Jan. 2004 to Jan. 2015, continuous 88 cases diagnosed with pulmonary metastases from CRC, including 15 cases of synchronous metastases and 73 metachronous metastases, were analyzed in the retrospective study.All of these 88 cases underwent curative pulmonary resection including 8 cases of simultaneous surgery. The one-year, three-year and five-year survival of the 88 cases were 93.4%, 60.2% and 35.7%, respectively. 63 patients just have one metastasis, and 25 patients have more than one metastasis. Additionally, the one-year, three-year and five-year survival was 98.1%, 70.2% and 40.3% respectively in one metastasis group, while 80.1%, 37.9% and 22.5% respectively in more than one metastasis group (p = 0.003). DFS of 37 metachronous metastases were equal or greater than 18 months, and DFS of 36 metachronous metastases were less than 18 months. The one-year, three-year and five-year survival was 97.8%, 77.9% and 41.4% respectively in the DFS≥18 month group, while 88.2%, 44.6% and 28.1% respectively in the DFS<18 month group (p = 0.01).Surgical resection of pulmonary metastases from colorectal cancer can improve survival rate in selected patients. It seems that the number of metastases is an independence prognostic factor in surgical treatment. Furthermore, longer DFI implies longer survival for resectable CRC pulmonary metastases.
    Keywords Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2017-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Effect of Low Tube Voltage on Image Quality, Radiation Dose, and Low-Contrast Detectability at Abdominal Multidetector CT

    Kun Tang / Ling Wang / Rui Li / Jie Lin / Xiangwu Zheng / Guoquan Cao

    Journal of Biomedicine and Biotechnology, Vol

    Phantom Study

    2012  Volume 2012

    Abstract: Purpose. To investigate the effect of low tube voltage (80 kV) on image quality, radiation dose, and low-contrast detectability (LCD) at abdominal computed tomography (CT). Materials and Methods. A phantom containing low-contrast objects was scanned with ...

    Abstract Purpose. To investigate the effect of low tube voltage (80 kV) on image quality, radiation dose, and low-contrast detectability (LCD) at abdominal computed tomography (CT). Materials and Methods. A phantom containing low-contrast objects was scanned with a CT scanner at 80 and 120 kV, with tube current-time product settings at 150–650 mAs. The differences between image noise, contrast-to-noise ratio (CNR), and scores of LCD obtained with 80 kV at 150–650 mAs and those obtained with 120 kV at 300 mAs were compared respectively. Results. The image noise substantially increased with low tube voltage. However, with identical dose, use of 80 kV resulted in higher CNR compared with CNR at 120 kV. There were no statistically significant difference in CNR and scores of LCD between 120 kV at 300 mAs and 80 kV at 550–650 mAs (>0.05). The relative dose delivered at 80 kV ranged from 58% at 550 mAs to 68% at 650 mAs. Conclusion. With a reduction of the tube voltage from 120 kV to 80 kV at abdominal CT, the radiation dose can be reduced by 32% to 42% without degradation of CNR and LCD.
    Keywords Biotechnology ; TP248.13-248.65 ; Medicine ; R
    Language English
    Publishing date 2012-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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