Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

Zon, Mike C M van; Waa, José D van der; Veta, Mitko; Krekels, Gertruud A. M.

doi:10.1111/exd.14306

Cited by 25 publications

(21 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hugo Bonnefille 1 Marwan Abbas 2 Pascal Roger 3 François Habib 1 Farzaneh Masset 3 Marc Chaumont 2,4 Gerard Subsol 2 Pierre E. Stoebner 1,5…”

Section: Data Ava I L a Bi L I T Y S Tat E M E N Tunclassified

“…

Dear Editor, Previous studies applied deep learning to Mohs micrographic surgery (MMS) by developing algorithms able to classify digitized histopathology slides as BCC positive or negative. [1][2][3] Because the histological location of residual tumour is necessary to guide subsequent excision, we aimed to develop and evaluate an algorithm using convolutional neural networks (CNNs) that would automatically localize and point out BCC tumour islands in digitized MMS histology slides. In this retrospective study (IRB protocol 22.01.16), a total of 246 haematoxylin and eosin-stained frozen section histology slides from 106 different patients were obtained from our Mohs

…”

mentioning

confidence: 99%

See 1 more Smart Citation

A deep‐learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections

Bonnefille¹,

Abbas

Roger³

et al. 2023

Acad Dermatol Venereol

View full text Add to dashboard Cite

“…Hugo Bonnefille 1 Marwan Abbas 2 Pascal Roger 3 François Habib 1 Farzaneh Masset 3 Marc Chaumont 2,4 Gerard Subsol 2 Pierre E. Stoebner 1,5…”

Section: Data Ava I L a Bi L I T Y S Tat E M E N Tunclassified

“…

…”

mentioning

confidence: 99%

A deep‐learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections

Bonnefille¹,

Abbas

Roger³

et al. 2023

Acad Dermatol Venereol

View full text Add to dashboard Cite

“…Combalia et al recently described a machine-learning assisted ex vivo CLSM pathology model, which was able to diagnose BCC with a sensitivity and specificity of 88% and 91%, respectively [ 71 ]. The model performance was even more satisfying when compared to analogous studies on digitally scanned H&E stained traditional pathology slides [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 92%

“…In order to further optimize the diagnostic process, deep learning models could assist the pathologists and Mohs surgeons to rapidly and precisely detect tumor ROI [ 62 , 63 ]. In the field of conventional pathology, various applications incorporating machine learning algorithms have generally shown a satisfactory performance to detect cell nuclei [ 64 ], mitosis [ 65 ], glands [ 8 ] and blood vessels [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Ruini

Schlingmann

Jonke³

et al. 2021

Cancers

View full text Add to dashboard Cite

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

show abstract

“…Jiang et al proposed a DL framework for BCC recognition and segmentation based on smartphone‐captured microscopic ocular images and WSIs 17 . Zon et al developed a DL model for BCC segmentation based on WSIs, with an average dice score of 0.66 and an average AUC of 0.90 18 . Therefore, the deep‐learning‐based segmentation task on NMSC still has vast space for development in terms of the tumor types and the performance of models.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based semantic segmentation of non‐melanocytic skin tumors in whole‐slide histopathological images

Wang

Shao

Huang

et al. 2023

Experimental Dermatology

View full text Add to dashboard Cite

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the two most common skin cancer and impose a huge medical burden on society. Histopathological examination based on whole-slide images (WSIs) remains to be the confirmatory diagnostic method for skin tumors. Accurate segmentation of tumor tissue in WSIs by deep-learning (DL) models can reduce the workload of pathologists and help surgeons ensure the complete removal of tumors. To accurately segment the tumor areas in WSIs of BCC, SCC and squamous cell papilloma (SCP, homologous to SCC) with robust models. We established a data set (ZJU-NMSC) containing 151 WSIs of BCC, SCC and SCP in total. Seven models were utilized to segment WSIs, including the stateof-the-art model, models proposed by us and other models. Dice score, intersection over union, accuracy, sensitivity and specificity were used to evaluate and compare the performance of different models. Heatmaps and tumor tissue masks were generated to reflect the results of the segmentation. The processing times of models are also recorded and compared. While the dice score of most models is higher than 0.85, deeplab v3+ has the best performance and the corresponding tumor tissue mask is more consistent with the ground truth tumor areas even with complex and small lobular lesions. This study broadens the use of DL-based segmentation models in WSIs of skin tumors in terms of tumor types and computational approaches. Segmenting tumor areas can simplify the process of histopathological inspection and benefit the diagnosis and following management of the diseases in practice.

show abstract

Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

Cited by 25 publications

References 18 publications

A deep‐learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections

A deep‐learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Deep learning‐based semantic segmentation of non‐melanocytic skin tumors in whole‐slide histopathological images

Contact Info

Product

Resources

About