Automating the ABCD Rule for Melanoma Detection: A Survey

Ali, Abder-Rahman; Li, Jingpeng; Yang, Guang

doi:10.1109/access.2020.2991034

Cited by 30 publications

(14 citation statements)

References 95 publications

(119 reference statements)

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“…Tumor shape, whether spherical or elongated into an ellipsoid and quantified by a metric called eccentricity (15), is not usually measured nor currently considered in staging for PCa, unlike breast (16,17), lung (18,19), and skin (20,21) cancers. Recently, however, a study (15) showed that spherical prostate adenocarcinoma shape, rather than elongated, shows a propensity for higher Gleason score and presumed greater aggressiveness.…”

Section: Introductionmentioning

confidence: 99%

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Mayer¹,

Simone²,

Türkbey³

et al. 2022

Quant Imaging Med Surg

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Background: Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Methods: Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrastenhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. Results: From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from R=-0.51 to -0.672 (P-values <0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from R=0.46 to 0.50 (P-values <0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Conclusions: Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histologybased analysis enhanced Gleason score prediction, unlike MRI.

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Section: Introductionmentioning

confidence: 99%

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Mayer¹,

Simone²,

Türkbey³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…However, this is not always the case as breast cancer patients with negative hormone receptor and positive HER2 expression show greater aggressiveness when they are more elongated and asymmetric (4). Similarly, in malignant melanomas (8,9), asymmetry in a tumor's shape may indicate its aggressiveness.…”

Section: Discussionmentioning

confidence: 99%

“…Staging components (1-3) such as intraprostatic tumor volumes/locations, metastatic extent, and lymph node involvement may indicate prostate tumor aggressiveness and provide important input for disease management. Tumor shape, whether spherical or elongated into an ellipsoid and quantified by a metric called eccentricity, is not usually measured and currently is not considered in staging for prostate cancer unlike breast (4,5), lung (6,7), and skin (8,9) cancers. Due to the relative absence of studies regarding tumor eccentricity in prostate cancer, definitive understanding of the relationship between tumor shape and potential cancer progression is limited and its contribution for assessing the prognosis of a patient is uncertain to date.…”

Section: Introductionmentioning

confidence: 99%

Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging

Mayer

Simone

Türkbey

et al. 2021

Quant Imaging Med Surg

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Background: Proliferating cancer cells interacting with their microenvironment affects a tumor's spatial shape. Elongation or roundness (eccentricity) of lung, skin, and breast cancers indicates the cancer's relative aggressiveness. Non-invasive determination of the prostate tumor's shape should provide meaningful input for prognostication and clinical management. There are currently few studies of prostate tumor shape, therefore this study examines the relationship between a prostate tumor's eccentricity, derived from spatially registered multi-parametric MRI and histology slides, and Gleason scores.Methods: A total of 26 consecutive patients were enrolled in the study. Median patient age was 60 years (range, 49 to 75 years), median PSA was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL, and median Gleason score was 7 (range, 6 to 9). Multi-parametric MRI (T1, T2, Diffusion, Dynamic Contrast Enhanced) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes. Multiparametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Also, tumor shape was computed from the histology slides. Blobbing, labeling, and calculation of eccentricity using moments of inertia were applied to the multi-parametric MRI and histology slides. The eccentricity measurements were compared to the Gleason scores from 25 patients.Results: From histology slides analysis: the correlation coefficient between the eccentricity for the largest blob and a weighted average eccentricity against the Gleason score ranged from −0.67 to −0.78 for all 18 patients whose tumor volume exceeded 1.0 cc. From multi-parametric MRI analysis: the correlation coefficient between the eccentricity for the largest blob for varying thresholds against the Gleason score ranged from −0.60 to −0.66 for all 25 patients showing contrast uptake in the Dynamic Contrast Enhancement (DCE) MRI.Conclusions: Spherical shape prostate adenocarcinoma shows a propensity for higher Gleason score. This novel finding follows lung and breast adenocarcinomas but depart from other primary tumor types. Analysis of multi-parametric MRI can non-invasively determine the prostate tumor's morphology and add critical information for prognostication and disease management. Eccentricity of smaller tumors (<1.0 cc) from MP-MRI correlates well with Gleason score, unlike eccentricity measured using histology of wholemount prostatectomy. 2 Mayer et al. Correlate prostate tumor eccentricity, Gleason

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“…ABCDE 7 is widely adopted by the nondermatologists for extracting features from dermoscopic images than the Glasgow 7‐point checklist 8 during the clinical screening process 9 . A lesion is assigned a score(s) by the ABCD rule, a conjugation of the scores for four distinct features, like A‐symmetry, B‐order, C‐olors, and D‐ifferential structures 10 .…”

Section: Introductionmentioning

confidence: 99%

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

Singh

Janghel

Sahu

2021

Int J Imaging Syst Tech

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A computer-aided-diagnostic system for diagnosing melanoma often uses distinct kinds of features for characterizing the lesions. Extracting distinct features from melanocytic images represents the various characteristics of pigmented lesions. Concatenating such features distinguishes extracted feature's information effectively while eliminating the redundant information amid them, which aids in discriminating the cancerous and noncancerous lesions. This article proposes a framework comprising segmentation, feature extraction, feature fusion, and classification to differentiate benign lesions from melanoma. The proposed framework is four-fold: beginning with the extraction of ROI from an image, the SLICACO method is used for segmentation. Thereafter, ABCD rule-based global and local features are extracted for effective melanoma detection. Further, we develop a new hybrid feature fusion strategy, PCAFA, leveraging the benefits of principal component analysis and factor analysis. The method performs early fusion by combining all extracted features within an individual feature vector and fed to a learning model for the prediction. While late fusion is performed using majority voting by combining the outputs of machine learning models. After that, gradient tree boosting, support vector machine, and decision tree models are trained to utilize distinct features of skin lesions for their classification as benign or malignant. The effectiveness of the designed framework is validated on the ISIC2017 benchmark skin lesion dataset based on specificity, sensitivity, and accuracy. The generalizability of the designed framework is gauged by conducting a fair comparison with conventional methods. Evaluated results reveal the potential of the proposed fused feature-set in discriminating malignant and nonmalignant lesions with an accuracy of 96.8%.

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Automating the ABCD Rule for Melanoma Detection: A Survey

Cited by 30 publications

References 95 publications

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

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