Differential diagnosis of squamous cell carcinoma in situ using skin histopathological images (2023)

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Computers in Biology and Medicine

Volume 70,

1 March 2016

, Pages 23-39

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https://doi.org/10.1016/j.compbiomed.2015.12.024Get rights and content

(Video) Histopathology Skin--Squamous cell carcinoma

Abstract

Differential diagnosis of squamous cell carcinoma in situ is of great importance for prognosis and decision making in the disease treatment procedure. Currently, differential diagnosis is done by pathologists based on examination of the histopathological slides under the microscope, which is time consuming and prone to inter and intra observer variability. In this paper, we have proposed an automated method for differential diagnosis of SCC in situ from actinic keratosis, which is known to be a precursor of squamous cell carcinoma. The process begins with epidermis segmentation and cornified layer removal. Then, epidermis axis is specified using the paths in its skeleton and the granular layer is removed via connected components analysis. Finally, diagnosis is done based on the classification result of intensity profiles extracted from lines perpendicular to the epidermis axis. The results of the study are in agreement with the gold standards provided by expert pathologists.

Introduction

Cutaneous squamous cell carcinoma (SCC) is the second most common form of skin cancer, constituting about 20% of all reported cases of non-melanoma skin malignancies [1], [2]. It arises from uncontrolled growth of squamous cells in the upper layer of the skin tissue and has the potential to metastasize to other organs of the body if left untreated. Squamous cell carcinoma in situ, also called intra-epidermal SCC or Bowen disease, is the first stage of SCC. “In situ” means that the malignant cells are still only in the epidermis and have not invaded deeper into the dermis [3].

Differential diagnosis is the procedure of distinguishing a particular disease from others that present similar clinical signs, e.g. differentiating between prostate cancer and benign prostatic hyperplasia. In the case of SCC in situ, there are number of skin lesions that should be considered in differential diagnosis, among which, actinic keratosis and basal cell carcinoma (BCC) are more remarkable [3].

Differential diagnosis of SCC in situ versus actinic keratosis is not a trivial task as both share similar clinical and histological features. Currently, differential diagnosis is done by pathologists based on examination of the histopathological slides under the microscope, which is laborious, time consuming and prone to inter and intra observer variability. Moreover, there are relatively few expert pathologists against the large number of tissue samples to be investigated. So, automated systems for analyzing the histopathological slides are desirable.

Actinic keratosis is commonly believed to be a precursor of squamous cell carcinoma in situ and is referred to as incipient SCC by some authors [4], because it can progress to invasive squamous cell carcinoma if left untreated. Hence, in this work, the problem of distinguishing SCC in situ from actinic keratosis is addressed.

Skin is composed of three primary layers: epidermis, dermis and hypodermis (subcutaneous adipose layer). Epidermis is divided into three layers: Malpighian (basal and squamous) layer, granular layer and cornified layer. Fig. 1a shows a histopathological image of normal skin in which the two main layers, epidermis and dermis are indicated. Cells existing in basal and squamous layer are called keratinocyte.

In pathology, SCC in situ is recognized by the presence of dysplastic keratinocytes which cover the full-thickness of epidermis and in advanced stages, invade the dermis [3]. Dysplastic keratinocytes are immature cells with a relatively high nucleus to cytoplasm (N:C) ratio, the ratio which indicates the maturity of a cell [5]. As a cell matures, this ratio generally decreases. Unlike healthy cells, cancerous cells reproduce very quickly and do not have a chance to mature. Other pathological cues to rule out SCC in situ are cells polymorphism, hyperkeratosis (thickened cornified layer), parakeratosis (presence of keratinocytes nucleus in cornified layer) and increase in epidermis layer thickness. Actinic keratosis is also characterized by the above mentioned cues specially dysplastic keratinocytes in epidermis layer, but it differs from SCC in situ in the way that abnormal cells present mainly in lower third of the epidermis [3], [6].

Fig. 1b and c shows, respectively, skin histopathological images of actinic keratosis and SCC in situ. In contrast to the normal case of Fig. 1a in which immature cells just cover a relatively thin layer at the bottom of the epidermis (basal layer), in actinic keratosis and SCC in situ cases, they cover the lower one-third and full thickness of the epidermis, respectively. Note also the atypia which presents among keratinocytes in the last two cases, in the sense that they are more polymorphic and of different sizes. White bracket indicates the area with atypical keratinocytes.

It should be mentioned that Fig. 1b and c are representative samples of actinic keratosis and SCC in situ which exhibit almost distinct appearances in texture of the epidermis. However, there are borderline cases of actinic keratosis in which dysplastic keratinocytes have covered, to some extent, the middle third part of the epidermis as well. Fig. 1d shows an example of this kind. This case is labeled as actinic keratosis by expert pathologists.

Although parakeratosis is usually less pronounced in actinic keratosis, but there are cases in which it is relatively more severe, resembling the appearance of SCC in situ. Consequently, parakeratosis cannot be used alone as a cue for differential diagnosis. Also note that from the images in Fig. 1, it can be observed that the cells in epidermis not only are overlapping and non homogenous, but also they are too cluttered to enable simple segmentation of individual cells, especially in the SCC in situ case.

The organization of this paper is as follows. A brief review of the works in the field of histopathological images analysis is presented in Section 2. The proposed technique is described in Section 3; results are demonstrated and discussed in Section 4, followed by conclusion in Section 5.

Section snippets

Related works

Histopathological images processing is a popular research field the importance of which has rapidly grown over the years due to increasing incidence of malignant diseases worldwide. There are many works in the literature which have covered the processing of histopathological images from various tissues. Cervical tissue is analyzed in [7], [8], [9], [10] to diagnose and grade cervical intraepithelial neoplasia (CIN). The automated method proposed in [7] consists of two stages. First, the input

Materials

For conducting this study, 30 histopathological images were obtained from skin tissue samples of unknown patients with the age between 40 and 60, sixteen images of SCC in situ and fourteen images of actinic keratosis. After sectioning the tissue samples at a thickness of about 5µm and staining using Hematoxylin and Eosin (H&E), high resolution images were digitally captured by Aperio ScanScope slide scanner from histopathology slides at 60× magnifications with pixel size of 0.28µm. From these

Results and discussion

Performance of the proposed method was evaluated in two parts: classification of samples and tumor area segmentation.

Conclusion and future works

In this paper, a method for differential diagnosis of squamous cell carcinoma in situ from actinic keratosis is proposed. After epidermis segmentation and cornified layer removal, epidermis axis is specified using the paths in its skeleton and granular layer is removed via connected components analysis. Finally, diagnosis is done based on the classification result of intensity profiles extracted from lines perpendicular to the epidermis axis. Evaluation results have shown that our method is

Conflict of interest statement

None declared.

Acknowledgment

The authors would like to thank Dr. Maryam Almassi, M.D., A.P.C.P, from Vanak Pathobiology Laboratory (Tehran, Iran) for insightful advices and helping us in medical aspects of this research.

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  • Cited by (11)

    • AI-based carcinoma detection and classification using histopathological images: A systematic review

      2022, Computers in Biology and Medicine

      Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists′ evaluations.

    • Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis

      2017, Tissue and Cell

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      Present study explores the possibility of segmentation of DIC images of oral epithelial cells through active contour snake model to distinguish cancer cells from normal. On the other hand, different analytical methods are used for prediction of pre-malignant trend, based on sequential changes in cellular and nuclear levels among the habitual smokers (Noroozi and Zakerolhosseini, 2016). However, according to the literature very few automated systems are available for oral cancer detection based on feature extraction (Krishna et al., 2012, 2010).

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      Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (−ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit.

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      An automated melanoma diagnosis system is presented by the authors in [23] based on the methods in [17,20] for melanocyte detection and epidermis segmentation. The problem of melanocytic tumor depth measurement is addressed in [21,24] and differential diagnosis of squamous cell carcinoma from actinic keratosis using time–frequency and connected component analysis is considered in [25]. The problem of basal cell carcinoma detection is addressed in [26–30].

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