Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma

Ashizawa, Kei; Yoshimura, Kenichi; Johno, Hisashi; Inoue, Tomohiro; Katoh, Ryohei; Funayama, Satoshi; Sakamoto, Kiyomi; Takeda, Sén; Masuyama, Keisuke; Matsuoka, Tomokazu; Ishii, Hiroki

doi:10.1016/j.oraloncology.2017.11.008

Cited by 23 publications

(22 citation statements)

References 33 publications

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“…Collectively, these results strongly indicate that this diagnostic system can distinguish breast cancer from normal breast tissue. Previous reports showed the usefulness of this system in discriminating squamous cell carcinoma of head and neck regions from normal mucosa, and the present study of breast cancer indicates that this technology can be applied to epithelial tissues regardless of their origin. Moreover, because breast cancer is heterogeneous and comprises glands and a large proportion of stroma, this study has revealed the versatility of the instrumentation.…”

Section: Resultssupporting

confidence: 66%

Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry

Iwano

Yoshimura

Inoue³

et al. 2020

British Journal of Surgery

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Probe electrospray ionization mass spectrometry (PESI‐MS) is an ambient ionization‐based mass spectrometry method that surpasses the original electrospray ionization technique in features such as the rapidity of analysis, simplicity of the equipment and procedure, and lower cost. This study found that the PESI‐MS system with machine learning has the potential to establish a lipid‐based diagnosis of breast cancer with higher accuracy, using a simpler approach. Rapid mass spectrometry for breast cancer

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Section: Resultssupporting

confidence: 66%

Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry

Iwano

Yoshimura

Inoue³

et al. 2020

British Journal of Surgery

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“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

Section: Discussionmentioning

confidence: 99%

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Tama

Kim

et al. 2020

Clin Exp Otorhinolaryngol

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Objectives. To present an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, with respect to opportunities, research challenges, and research directions.Methods. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles; we excluded non-English publications and duplicates, which resulted in a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments.Results. Most studies (42.22%, 38/90) used AI for image-based analysis, followed by clinical diagnosis and treatments (24 studies); each of the remaining two subcategories included 14 studies.Conclusion. Machine and deep learning have been extensively applied in the field of otorhinolaryngology. However, performance varies and research challenges remain.

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“…Ambient ionization unit (DPiMS-8060; Shimadzu Corp.) was used for PESI combined with a triple quadrupole mass spectrometer (LCMS-8060; Shimadzu Corp.) for direct MS, and the analyses were performed as previously described. (12) Analyses were performed for both positive and negative ion mode. The probe needle with a tip radius of < 1 µm was moved downward to touch the tissue sample and then upward to apply high voltage (2.3 kV for positive and − 2.0 kV for negative ion mode) for ESI.…”

Section: Pesi-ms and Machine Learningmentioning

confidence: 99%

“…(11) The obtained mass spectra are processed using machine learning algorithms such as logistic regression or support vector machines to discriminate cancer from non-cancer tissues. (12) Previous experiments demonstrated high discriminating power for hepatocellular carcinoma and renal cell carcinoma. (10,11) PESI-MS and machine learning is a cutting-edge diagnostic tool that can detect the difference in lipid pro les between various cancerous and non-cancerous tissues.…”

Section: Introductionmentioning

confidence: 99%

A New Rapid Diagnostic System with Ambient Mass Spectrometry and Machine Learning for Colorectal Liver Metastasis

Kiritani

Yoshimura

Arita

et al. 2020

Preprint

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Background: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM).Methods: A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail.Results: This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P<0.01, respectively). The proportion of phospholipids categorized as monounsaturated fatty acids was higher in CRLM (37.2%) than in non-cancer liver parenchyma (10.7%; P<0.01).Conclusion: The combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM.

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Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma

Cited by 23 publications

References 33 publications

Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry

Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

A New Rapid Diagnostic System with Ambient Mass Spectrometry and Machine Learning for Colorectal Liver Metastasis

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