A fractal based approach to evaluate the progression of esophageal squamous cell dysplasia

Hosseini, Zahra; Gholpayeghani, Seyed Mohammad Reza Hashemi; Sotoudeh, Masoud

doi:10.1016/j.bspc.2018.09.001

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Esophagus SCC (ESCC) and esophagus adenocarcinoma (EAC) are two major variants of EC. In western countries, EAC is the most common variant, and ESCC is dominant in developing countries [64]. Das et al [26] proposed CNN-based SCC grade classification of esophagus and oral tissue images.…”

Section: Esophagus Sccmentioning

confidence: 99%

“…Das et al [26] proposed CNN-based SCC grade classification of esophagus and oral tissue images. Hosseini et al [64] differentiated precancer of ESCC, called squamous cell dysplasia, from normal tissue. They considered the images of two different magnifications and enhanced the G component of RGB images using CLAHE technique.…”

Section: Esophagus Sccmentioning

confidence: 99%

“…Graph search [61] Morphological features [59,61], Statistical, structural and spectral features [60], ConvNet features [62,63] Transfer learning model [58], Statistical test [59], Machine learning algorithms [60,62], Ensemble network [63] Skin -Connected components analysis [56] Intensity features [56], Ztransform features [57] Machine learning algorithms [56,57] Esophagus CLAHE [64] -Histogram variations, epithelial thickness, Morphology features [64] Machine learning algorithms [64] their studies to extract the features. For instance, Hosseini et al [64] trained a classification model separately by taking the images from 10X and 20X magnifications and proposed a fractal-based approach to differentiate normal and esophagus dysplasia. Kumar et al [108] studied the influence of magnification in carcinoma classification.…”

Section: Cervicalmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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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.

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Section: Esophagus Sccmentioning

confidence: 99%

Section: Esophagus Sccmentioning

confidence: 99%

Section: Cervicalmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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“…These metrics are dimensionless numbers specialized for quantifying morphological complexity. Fractal analysis shows its utility in computer-aided diagnosis in histology images [17] and prognostic potential when applied to computed tomography (CT) [18,19] and positron emission tomography (PET) scanning [20] for the textures of esophageal cancer.…”

mentioning

confidence: 99%

Morphological Complexity of Intrapapillary Capillary Loops for Predicting the Invasion Depth of Superficial Esophageal Neoplasms Using Narrow‐Band Imaging

Hwang,

Park,

Park

et al. 2024

Int J Imaging Syst Tech

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Predicting the invasion depth of superficial esophageal neoplasms (SENs) is crucial for an indication toward endoscopic treatment. We aimed to develop imaging biomarkers to predict the invasion depth of SENs using the intrapapillary capillary loop (IPCL) patterns. Texture and fractal analyses were performed on IPCL demarcations. Logistic regression models were built to distinguish narrow‐band images (NBIs) of T1b from T1a and high‐grade dysplasia (HGD). Classification performances of a base model composed of the age, sex, height, and body mass index of the patient, and a morphometric model comprising the texture and fractal metrics along with the base model parameters were compared. NBIs of 39 patients including T staging of 6 HGD, 22 T1a, and 11 T1b were analyzed. In the logistic regression by a multivariable generalized linear model classifying histological depths, only texture entropy was significantly and positively associated with the invasion depth. Lower texture entropy of IPCL demarcations was significantly associated with diagnoses of T1b (odds ratio: 0.144, 95% confidence interval: 0.019–0.687) in comparison to the stratum with higher texture entropy. The performance of the morphometric model represented by areas under the receiver operating curves (0.795) was better than that of the base model (0.562). We suggest morphometric analyses, particularly texture entropy, have the potential as image biomarkers to aid in the prediction of SEN invasion depth using IPCL patterns.

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Esophageal epithelium modeling based on globally coupled map: an approach toward precancerous lesion diagnosis

Hosseini

Golpayegani

2020

Med Biol Eng Comput

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A fractal based approach to evaluate the progression of esophageal squamous cell dysplasia

Cited by 7 publications

References 37 publications

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Morphological Complexity of Intrapapillary Capillary Loops for Predicting the Invasion Depth of Superficial Esophageal Neoplasms Using Narrow‐Band Imaging

Esophageal epithelium modeling based on globally coupled map: an approach toward precancerous lesion diagnosis

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