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Personalized digital pipeline for basal cell carcinoma delineation using hyperspectral imaging

Ibek, Paulina LU ; Degener, Alexander LU orcid ; Stridh, Magne LU ; Tegnér, Marcus LU orcid ; Edén, Patrik LU ; Olariu, Victor LU ; Malmsjö, Malin LU orcid and Merdasa, Aboma LU orcid (2026) In Biomedical Optics Express 17(9). p.4916-4932
Abstract

Basal cell carcinoma (BCC) is the most common form of skin cancer with the current diagnostic procedures relying on surgical excisions and histopathological analyses. Incomplete removal of the tumor necessitates additional surgery, which is both costly and time-consuming. To address this, non-invasive diagnostic techniques capable of determining the dimensions of skin cancer preoperatively are urgently needed. This work presents the development of an automated diagnostic pipeline for delineating BCC, using hyperspectral imaging (HSI), combined with machine learning (ML). HSI is a non-invasive, high-resolution imaging technique capable of differentiating tissue structures based on their molecular composition. ML is a branch of AI where... (More)

Basal cell carcinoma (BCC) is the most common form of skin cancer with the current diagnostic procedures relying on surgical excisions and histopathological analyses. Incomplete removal of the tumor necessitates additional surgery, which is both costly and time-consuming. To address this, non-invasive diagnostic techniques capable of determining the dimensions of skin cancer preoperatively are urgently needed. This work presents the development of an automated diagnostic pipeline for delineating BCC, using hyperspectral imaging (HSI), combined with machine learning (ML). HSI is a non-invasive, high-resolution imaging technique capable of differentiating tissue structures based on their molecular composition. ML is a branch of AI where algorithms learn patterns from data to make predictions without being explicitly programmed. The proposed pipeline is designed to adapt for individual patients, as universal skin cancer characteristic signals are challenging to define. The pipeline predicted tumor sizes with high accuracy, as validated by histopathological measurements in the 21 samples evaluated. The median absolute errors were low (0.25–0.30 mm), meaning that the predicted tumor dimensions were within the clinically accepted safety margins for radical excisions (≤0.4 mm). This work is the first to use unsupervised ML for tumor delineation in BCC using HSI, which enables personalized preoperative assessment and facilitates surgical precision.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Biomedical Optics Express
volume
17
issue
9
pages
17 pages
publisher
Optical Society of America
external identifiers
  • scopus:105051435674
  • pmid:42769710
ISSN
2156-7085
DOI
10.1364/BOE.592641
language
English
LU publication?
yes
id
2b40fa53-cdd9-4e25-b9a3-03e06a778902
date added to LUP
2026-09-10 11:39:18
date last changed
2026-09-30 11:30:57
@article{2b40fa53-cdd9-4e25-b9a3-03e06a778902,
  abstract     = {{<p>Basal cell carcinoma (BCC) is the most common form of skin cancer with the current diagnostic procedures relying on surgical excisions and histopathological analyses. Incomplete removal of the tumor necessitates additional surgery, which is both costly and time-consuming. To address this, non-invasive diagnostic techniques capable of determining the dimensions of skin cancer preoperatively are urgently needed. This work presents the development of an automated diagnostic pipeline for delineating BCC, using hyperspectral imaging (HSI), combined with machine learning (ML). HSI is a non-invasive, high-resolution imaging technique capable of differentiating tissue structures based on their molecular composition. ML is a branch of AI where algorithms learn patterns from data to make predictions without being explicitly programmed. The proposed pipeline is designed to adapt for individual patients, as universal skin cancer characteristic signals are challenging to define. The pipeline predicted tumor sizes with high accuracy, as validated by histopathological measurements in the 21 samples evaluated. The median absolute errors were low (0.25–0.30 mm), meaning that the predicted tumor dimensions were within the clinically accepted safety margins for radical excisions (≤0.4 mm). This work is the first to use unsupervised ML for tumor delineation in BCC using HSI, which enables personalized preoperative assessment and facilitates surgical precision.</p>}},
  author       = {{Ibek, Paulina and Degener, Alexander and Stridh, Magne and Tegnér, Marcus and Edén, Patrik and Olariu, Victor and Malmsjö, Malin and Merdasa, Aboma}},
  issn         = {{2156-7085}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{9}},
  pages        = {{4916--4932}},
  publisher    = {{Optical Society of America}},
  series       = {{Biomedical Optics Express}},
  title        = {{Personalized digital pipeline for basal cell carcinoma delineation using hyperspectral imaging}},
  url          = {{http://dx.doi.org/10.1364/BOE.592641}},
  doi          = {{10.1364/BOE.592641}},
  volume       = {{17}},
  year         = {{2026}},
}