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Lin J, Yuan P, Lin R, Xue X, Chen M, Xing L. A Self-Powered Lactate Sensor Based on the Piezoelectric Effect for Assessing Tumor Development. SENSORS (BASEL, SWITZERLAND) 2024; 24:2161. [PMID: 38610372 PMCID: PMC11014382 DOI: 10.3390/s24072161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.
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Affiliation(s)
- Jiayan Lin
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.L.); (P.Y.); (R.L.); (X.X.)
| | - Pengcheng Yuan
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.L.); (P.Y.); (R.L.); (X.X.)
| | - Rui Lin
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.L.); (P.Y.); (R.L.); (X.X.)
| | - Xinyu Xue
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.L.); (P.Y.); (R.L.); (X.X.)
| | - Meihua Chen
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China;
| | - Lili Xing
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.L.); (P.Y.); (R.L.); (X.X.)
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Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K. Clinical Impact of Deep Learning Reconstruction in MRI. Radiographics 2023; 43:e220133. [PMID: 37200221 DOI: 10.1148/rg.220133] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio. When applied to lower magnetic field-strength scanners, the signal-to-noise ratio can be increased without extending the imaging time, and image quality is comparable to that of higher-field-strength scanners. Shorter imaging times decrease patient discomfort and reduce MRI scanner running costs. The incorporation of DLR into accelerated acquisition imaging techniques, such as parallel imaging or compressed sensing, shortens the reconstruction time. DLR is based on supervised learning using convolutional layers and is divided into the following three categories: image domain, k-space learning, and direct mapping types. Various studies have reported other derivatives of DLR, and several have shown the feasibility of DLR in clinical practice. Although DLR efficiently reduces Gaussian noise from MR images, denoising makes image artifacts more prominent, and a solution to this problem is desired. Depending on the training of the convolutional neural network, DLR may change the imaging features of lesions and obscure small lesions. Therefore, radiologists may need to adopt the habit of questioning whether any information has been lost on images that appear clean. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Shigeru Kiryu
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Hiroyuki Akai
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Koichiro Yasaka
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Taku Tajima
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Akira Kunimatsu
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Naoki Yoshioka
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Masaaki Akahane
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Osamu Abe
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Kuni Ohtomo
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
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Kohada Y, Satani N, Kaiho Y, Iwamura H, Sakamoto T, Kusumoto H, Kukimoto T, Oikawa M, Mikami J, Ito J, Matsuura T, Hinata N, Koyama K, Sato M. Novel quantitative software for automatically excluding red bone marrow on whole‐body magnetic resonance imaging in patients with metastatic prostate cancer: A pilot study. Int J Urol 2022; 30:356-364. [PMID: 36539348 DOI: 10.1111/iju.15124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To establish a novel quantitative method that automatically excludes the red bone marrow and accurately quantifies the tumor volume on whole-body magnetic resonance imaging using updated imaging software. To also evaluate the association between the quantified tumor volume and the prognosis of patients with metastatic prostate cancer. METHODS This prospective analysis included patients diagnosed with metastatic hormone-sensitive or metastatic castration-resistant prostate cancer between 2017 and 2022. We developed an imaging software (Attractive BD_Score) that analyzed whole-body diffusion-weighted and in-phase and opposed-phase T1-weighted images to automatically exclude the red bone marrow. The quantified tumor volume was compared with that quantified by traditional whole-body diffusion-weighted imaging without red bone marrow exclusion. Prostate-specific antigen progression-free survival, time-to-pain progression, and overall survival were evaluated to assess the prognostic value of the quantified tumor volume. RESULTS The quantified tumor volume was significantly smaller than that quantified by the traditional method in metastatic hormone-sensitive (median: 81.0 ml vs. 149.4 ml) and metastatic castration-resistant (median: 29.4 ml vs. 63.5 ml) prostate cancer. A highly quantified tumor volume was associated with prostate-specific antigen progression-free survival (p = 0.030), time-to-pain progression (p = 0.003), and overall survival (p = 0.005) in patients with metastatic hormone-sensitive prostate cancer and with poor prostate-specific antigen progression-free survival (p = 0.001) and time-to-pain progression (p = 0.005) in patients with metastatic castration-resistant prostate cancer. CONCLUSIONS Our imaging method could accurately quantify the tumor volume in patients with metastatic prostate cancer. The quantified tumor volume can be clinically applied as a new prognostic biomarker for metastatic prostate cancer.
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Affiliation(s)
- Yuki Kohada
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
- Department of Urology Hiroshima University Graduate School of Biomedical Sciences Hiroshima Japan
| | - Nozomi Satani
- Division of Radiology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Yasuhiro Kaiho
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Hiromichi Iwamura
- Department of Urology Hirosaki University Graduate School of Medicine Hirosaki Japan
| | | | - Hiroki Kusumoto
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Takashi Kukimoto
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Masaaki Oikawa
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Jotaro Mikami
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Jun Ito
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Tomonori Matsuura
- Division of Radiology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Nobuyuki Hinata
- Department of Urology Hiroshima University Graduate School of Biomedical Sciences Hiroshima Japan
| | - Kaneki Koyama
- Division of Radiology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
| | - Makoto Sato
- Division of Urology, Faculty of Medicine Tohoku Medical and Pharmaceutical University Sendai Japan
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Tajima T, Akai H, Sugawara H, Furuta T, Yasaka K, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K, Kiryu S. Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer. Magn Reson Imaging 2022; 92:169-179. [PMID: 35772583 DOI: 10.1016/j.mri.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/04/2022] [Accepted: 06/23/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR). METHODS Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis. RESULTS The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types. CONCLUSIONS dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.
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Affiliation(s)
- Taku Tajima
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan
| | - Hiroyuki Akai
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan; Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Haruto Sugawara
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Toshihiro Furuta
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Koichiro Yasaka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Akira Kunimatsu
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, 2600-1 kitakanamaru, Otawara, Tochigi 324-8501, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
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