Comprehensive Histopathology and Fast Cancer Imaging in Pancreatic Biopsies: Infrared Imaging with Machine Learning Approach

Abstract

Infrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complementary information source for more classical histopathology. In this article we report such results for pancreatic cancer histopathology based on IR imaging and machine learning, providing a classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. This study forms a steppingstone for further avenues and we also report initial results in two of them. The first is a translation to a fast Quantum Cascade Laser microscope for intraoperative margin detection with a timescale in minutes. The second is subtle pathology grading, placing a biochemically grounded baseline for neoplasia (PanIN1-2) in the benign/cancerous space.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Pancreatic cancer comprehensive histopathology based on IR chemical imaging project, which was carried out within the Homing programme (grant no. Homing/2016-2/20) of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was granted by Ethics committee at Jagiellonian University in Krakow (no. 1072.6120.304.2020).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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