Dataset Information

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

ABSTRACT: Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specificity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

INSTRUMENT(S): ultraflex

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Colon

DISEASE(S): Colon Cancer

SUBMITTER: Parul Mittal

LAB HEAD: Prof Peter Hoffmann

PROVIDER: PXD019662 | Pride | 2022-02-15

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	1_UFX_UniSA_00053-P100031-180823-PM06-WEHI-TMA5D-01.dat	Other
	1_UFX_UniSA_00058-P100031-180904-PM06-WEHI-TMA5F-01.dat	Other
	1_UFX_UniSA_00059-P100031-180905-PM06-WEHI-TMA5B-01.dat	Other
	2-UFX_UniSA_00052-P100031-180822-PM06-WEHI-TMA5E-02.dat	Other
	3_UFX_UniSA_00064-P100031-180927-PM06-WEHI-TMA7B-01.dat	Other

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Publications

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

Mittal Paul P Condina Mark R MR Klingler-Hoffmann Manuela M Kaur Gurjeet G Oehler Martin K MK Sieber Oliver M OM Palmieri Michelle M Kommoss Stefan S Brucker Sara S McDonnell Mark D MD Hoffmann Peter P

Cancers 20211027 21

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated c ...[more]

PMID: 34771551

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