Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review
Abstract
:1. Introduction
- The box-counting method is based on a grid of multiple small boxes at different scales superimposed on the fractal object to analyze and count the boxes needed to cover the object; the relationship between box size and the number of boxes gives the FD.
- Box-counting in 2D (Minkowski–Bouligand dimension) is an extension of the box-counting method wherein the box size is varied, and the relationship between box size and the number of boxes is analyzed.
- Box-counting in 3D is an extension of the box-counting method to three-dimensional objects. Three-dimensional boxes are used to cover the fractal, and the relationship between box size and the number of boxes is analyzed.
2. Research Methods
2.1. Protocol, Focused Question
2.2. Search and Selection Strategy
- -
- The study population (P) consists of humans with oral cancer and oral potentially malignant disorders;
- -
- The intervention (I) was fractal analysis for diagnostic purposes;
- -
- The comparison (C) was with the conventional gold standard diagnosis;
- -
- The outcome (O) was the evaluation of the capability of fractal analysis to diagnose oral cancer and distinguish among oral cancer, oral potentially malignant disorders, and non-neoplastic oral lesions and diseases;
- -
- The study designs (S) included cross-sectional studies, retrospective cohort studies, prospective comparative studies, case-control studies, case series, and case reports.
3. Characteristics of the Studies
3.1. FA Methodologies and Tools
3.2. Fractal Objects
4. Fractal Analysis to Support Oral Cancer and OPMDs Diagnosis and Prognosis
Limitations of the Studies
5. Discussion and Conclusions
6. Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Miranda-Filho, A.; Bray, F. Global Patterns and Trends in Cancers of the Lip, Tongue and Mouth. Oral Oncol. 2020, 102, 104551. [Google Scholar] [CrossRef] [PubMed]
- Warnakulasuriya, S. Oral Potentially Malignant Disorders: A Comprehensive Review on Clinical Aspects and Management. Oral Oncol. 2020, 102, 104550. [Google Scholar] [CrossRef] [PubMed]
- Mauceri, R.; Bazzano, M.; Coppini, M.; Tozzo, P.; Panzarella, V.; Campisi, G. Diagnostic Delay of Oral Squamous Cell Carcinoma and the Fear of Diagnosis: A Scoping Review. Front. Psychol. 2022, 13, 1009080. [Google Scholar] [CrossRef] [PubMed]
- Contaldo, M.; Di Napoli, A.; Pannone, G.; Franco, R.; Ionna, F.; Feola, A.; De Rosa, A.; Santoro, A.; Sbordone, C.; Longo, F.; et al. Prognostic Implications of Node Metastatic Features in OSCC: A Retrospective Study on 121 Neck Dissections. Oncol. Rep. 2013, 30, 2697–2704. [Google Scholar] [CrossRef]
- Mascitti, M.; Togni, L.; Caponio, V.C.A.; Zhurakivska, K.; Bizzoca, M.E.; Contaldo, M.; Serpico, R.; Lo Muzio, L.; Santarelli, A. Lymphovascular Invasion as a Prognostic Tool for Oral Squamous Cell Carcinoma: A Comprehensive Review. Int. J. Oral Maxillofac. Surg. 2021, 51, 1–9. [Google Scholar] [CrossRef]
- Chasma, F.; Pedr King, R.; Ker, S.Y. Are There Diagnostic Alternatives to Histopathology in Detecting Oral Cancer? Evid. Based Dent. 2022, 23, 24–25. [Google Scholar] [CrossRef]
- Anders, P.L.; Davis, E.L.; Reuland-Bosma, W.; Van Dijk, J.; Nakagawa, I.; Amano, A.; Ohara-Nemoto, Y.; Endoh, N.; Morisaki, I.; Kimura, S.; et al. Electronic Cigarette: Users Profile, Utilization, Satisfaction and Perceived Efficacy. Spec. Care Dent. 2015, 11, 30–35. [Google Scholar] [CrossRef]
- Mauceri, R.; Coppini, M.; Vacca, D.; Bertolazzi, G.; Panzarella, V.; Di Fede, O.; Tripodo, C.; Campisi, G. Salivary Microbiota Composition in Patients with Oral Squamous Cell Carcinoma: A Systematic Review. Cancers 2022, 14, 5441. [Google Scholar] [CrossRef]
- Romano, A.; Di Stasio, D.; Petruzzi, M.; Fiori, F.; Lajolo, C.; Santarelli, A.; Lucchese, A.; Serpico, R.; Contaldo, M. Noninvasive Imaging Methods to Improve the Diagnosis of Oral Carcinoma and Its Precursors: State of the Art and Proposal of a Three-Step Diagnostic Process. Cancers 2021, 13, 2864. [Google Scholar] [CrossRef]
- Sreeshma, B.; Sivakumar, D.; Maiti, D.; Devi, A. Biomarkers in the Progression and Metastasis of Oral Squamous Cell Carcinoma. J. Stem Cells 2022, 16, 127–161. [Google Scholar]
- Penedo, F.J.; Oswald, L.B.; Kronenfeld, J.P.; Garcia, S.F.; Cella, D.; Yanez, B. The Increasing Value of EHealth in the Delivery of Patient-Centred Cancer Care. Lancet Oncol. 2020, 21, e240–e251. [Google Scholar] [CrossRef] [PubMed]
- Lopes, R.; Betrouni, N. Fractal and multifractal analysis: A review. Med. Image Anal. 2009, 13, 634–649. [Google Scholar] [CrossRef] [PubMed]
- Bisoi, A.K.; Mishra, J. On calculation of fractal dimension of images. Pattern Recognit. Lett. 2001, 22, 631–637. [Google Scholar] [CrossRef]
- Salwaji, S.; Pasupuleti, M.K.; Manyam, R.; Pasupuleti, S.; Alapati, N.S.; Birajdar, S.S.; Mounika, R. Nuclear Fractal Dimension in Diagnosing Oral Cancer-A Systematic Review. Uttar Pradesh J. Zool. 2023, 44, 47–55. [Google Scholar] [CrossRef]
- Panigrahi, S.; Rahmen, J.; Panda, S.; Swarnkar, T. Fractal Geometry for Early Detection and Histopathological Analysis of Oral Cancer. In Proceedings of the 7th International Conference, MIKE 2019, Goa, India, 19–22 December 2020; Volume 11987, pp. 177–185. [Google Scholar]
- Sahoo, G.R.; Bharti, D.; Pradhan, A. Multifractal Analysis of Low Coherence Spectra for Oral Cancer Detection. In Proceedings of the SPIE BiOS, San Francisco, CA, USA, 1–6 February 2020; Volume 11253. [Google Scholar]
- Delides, A.; Panayiotides, I.; Alegakis, A.; Kyroudi, A.; Banis, C.; Pavlaki, A.; Helidonis, E.; Kittas, C. Fractal Dimension as a Prognostic Factor for Laryngeal Carcinoma. Anticancer Res. 2005, 25, 2141–2144. [Google Scholar]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Saaiq, M.; Ashraf, B. Modifying “Pico” Question into “Picos” Model for More Robust and Reproducible Presentation of the Methodology Employed in A Scientific Study. World J. Plast. Surg. 2017, 6, 390–392. [Google Scholar]
- Krishnan, M.M.R.; Shah, P.; Chakraborty, C.; Ray, A.K. Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images. J. Med. Syst. 2012, 36, 865–881. [Google Scholar] [CrossRef] [PubMed]
- Raja, J.V.; Khan, M.; Ramachandra, V.K.; Al-Kadi, O. Texture Analysis of CT Images in the Characterization of Oral Cancers Involving Buccal Mucosa. Dentomaxillofac. Radiol. 2012, 41, 475–480. [Google Scholar] [CrossRef]
- Pandey, P.; Kandakurti, S.; Saxena, V.; Tripathi, P.; Pamula, R.; Yadav, M. Fractal Analysis in Oral Leukoplakia. J. Indian Acad. Oral Med. Radiol. 2015, 27, 354–358. [Google Scholar] [CrossRef]
- Yinti, S.R.; Srikant, N.; Boaz, K.; Lewis, A.J.; Ashokkumar, P.J.; Kapila, S.N. Nuclear Fractal Dimensions as a Tool for Prognostication of Oral Squamous Cell Carcinoma. J. Clin. Diagn. Res. 2015, 9, EC21–EC25. [Google Scholar] [CrossRef] [PubMed]
- Phulari, R.; Rathore, R.; Talegaon, T. Nuclear Fractal Dimension: A New Objective Approach for Discriminating Normal Mucosa, Dysplasia and Carcinoma. J. Oral Maxillofac. Pathol. 2016, 20, 400–404. [Google Scholar] [CrossRef]
- Das, D.K.; Mitra, P.; Chakraborty, C.; Chatterjee, S.; Maiti, A.K.; Bose, S. Computational Approach for Mitotic Cell Detection and Its Application in Oral Squamous Cell Carcinoma. Multidimens. Syst. Signal Process. 2017, 28, 1031–1050. [Google Scholar] [CrossRef]
- Iqbal, J.; Patil, R.; Khanna, V.; Tripathi, A.; Singh, V.; Munshi, M.A.I.; Tiwari, R. Role of Fractal Analysis in Detection of Dysplasia in Potentially Malignant Disorders. J. Fam. Med. Prim. Care 2020, 9, 2448–2453. [Google Scholar] [CrossRef]
- Nawn, D.; Pratiher, S.; Chattoraj, S.; Chakraborty, D.; Pal, M.; Paul, R.R.; Dutta, S.; Chatterjee, J. Multifractal Alterations in Oral Sub-Epithelial Connective Tissue during Progression of Pre-Cancer and Cancer. IEEE J. Biomed. Health Inform. 2021, 25, 152–162. [Google Scholar] [CrossRef]
- Sharma, N.; Nawn, D.; Pratiher, S.; Shome, S.; Chatterjee, R.; Biswas, K.; Pal, M.; Paul, R.R.; Dutta, S.; Chatterjee, J. Multifractal Texture Analysis of Salivary Fern Pattern for Oral Pre-Cancers and Cancer Assessment. IEEE Sens. J. 2021, 21, 9333–9340. [Google Scholar] [CrossRef]
- Santolia, D.; Dahiya, S.; Sharma, S.; Khan, M.A.; Mohammed, N.; Priya, H.; Gupta, S.R.; Bhargava, S.; Gupta, D.S.R. Fractal Dimension and Radiomorphometric Analysis of Orthopanoramic Radiographs in Patients with Tobacco and Areca Nut Associated Oral Mucosal Lesions: A Pilot in-Vivo Study in a North Indian Cohort. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2022, 134, 627–638. [Google Scholar] [CrossRef]
- Krishnan, M.M.R.; Shah, P.; Choudhary, A.; Chakraborty, C.; Paul, R.R.; Ray, A.K. Textural Characterization of Histopathological Images for Oral Sub-Mucous Fibrosis Detection. Tissue Cell 2011, 43, 318–330. [Google Scholar] [CrossRef]
- Rahman, J.; Panda, S.; Panigrahi, S.; Mohanty, N.; Swarnkar, T.; Mishra, U. Perspective of Nuclear Fractal Dimension in Diagnosis and Prognosis of Oral Squamous Cell Carcinoma. J. Oral Maxillofac. Pathol. 2022, 26, 127. [Google Scholar] [CrossRef]
- Goutzanis, L.; Papadogeorgakis, N.; Pavlopoulos, P.M.; Katti, K.; Petsinis, V.; Plochoras, I.; Pantelidaki, C.; Kavantzas, N.; Patsouris, E.; Alexandridis, C. Nuclear Fractal Dimension as a Prognostic Factor in Oral Squamous Cell Carcinoma. Oral Oncol. 2008, 44, 345–353. [Google Scholar] [CrossRef]
- Goutzanis, L.P.; Papadogeorgakis, N.; Pavlopoulos, P.M.; Petsinis, V.; Plochoras, I.; Eleftheriadis, E.; Pantelidaki, A.; Patsouris, E.; Alexandridis, C. Vascular Fractal Dimension and Total Vascular Area in the Study of Oral Cancer. Head Neck 2009, 31, 298–307. [Google Scholar] [CrossRef] [PubMed]
- Spyridonos, P.; Gaitanis, G.; Tzaphlidou, M.; Bassukas, I.D. Spatial Fuzzy C-Means Algorithm with Adaptive Fuzzy Exponent Selection for Robust Vermilion Border Detection in Healthy and Diseased Lower Lips. Comput. Methods Programs Biomed. 2014, 114, 291–301. [Google Scholar] [CrossRef]
- Spyridonos, P.; Gaitanis, G.; Bassukas, I.D.; Tzaphlidou, M. Evaluation of Vermillion Border Descriptors and Relevance Vector Machines Discrimination Model for Making Probabilistic Predictions of Solar Cheilosis on Digital Lip Photographs. Comput. Biol. Med. 2015, 63, 11–18. [Google Scholar] [CrossRef] [PubMed]
- Lucchese, A.; Gentile, E.; Capone, G.; De Vico, G.; Serpico, R.; Landini, G. Fractal analysis of mucosal microvascular patterns in oral lichen planus: A preliminary study. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2015, 120, 609–615. [Google Scholar] [CrossRef] [PubMed]
- Mincione, G.; Di Nicola, M.; Di Marcantonio, M.C.; Muraro, R.; Piattelli, A.; Rubini, C.; Penitente, E.; Piccirilli, M.; Aprile, G.; Perrotti, V.; et al. Nuclear fractal dimension in oral squamous cell carcinoma: A novel method for the evaluation of grading, staging, and survival. J. Oral Pathol. Med. 2015, 44, 680–684. [Google Scholar] [CrossRef]
- D’Addazio, G.; Artese, L.; Traini, T.; Rubini, C.; Caputi, S.; Sinjari, B. Immunohistochemical study of osteopontin in oral squamous cell carcinoma allied to fractal dimension. J. Biol. Regul. Homeost. Agents 2018, 32, 1033–1038. [Google Scholar]
- Landini, G.; Rippin, J.W. An “asymptotic fractal” approach to the morphology of malignant cell nuclei. Fractals 1993, 1, 326–335. [Google Scholar] [CrossRef]
- Margaritescu, C.; Raica, M.; Pirici, D.; Simionescu, C.; Mogoanta, L.; Stinga, A.C.; Stinga, A.S.; Ribatti, D. Podoplanin Expression in Tumor-Free Resection Margins of Oral Squamous Cell Carcinomas: An Immunohistochemical and Fractal Analysis Study. Histol. Histopathol. 2010, 25, 701–711. [Google Scholar]
- Klatt, J.; Gerich, C.E.; Gröbe, A.; Opitz, J.; Schreiber, J.; Hanken, H.; Salomon, G.; Heiland, M.; Kluwe, L.; Blessmann, M. Fractal Dimension of Time-Resolved Autofluorescence Discriminates Tumour from Healthy Tissues in the Oral Cavity. J. Cranio-Maxillo-Facial Surg. Off. Publ. Eur. Assoc. Cranio-Maxillo-Facial Surg. 2014, 42, 852–854. [Google Scholar] [CrossRef]
- Bose, P.; Brockton, N.T.; Guggisberg, K.; Nakoneshny, S.C.; Kornaga, E.; Klimowicz, A.C.; Tambasco, M.; Dort, J.C. Fractal Analysis of Nuclear Histology Integrates Tumor and Stromal Features into a Single Prognostic Factor of the Oral Cancer Microenvironment. BMC Cancer 2015, 15, 409. [Google Scholar] [CrossRef]
- Ou-Yang, M.; Hsieh, Y.-F.; Lee, C.-C. Biopsy Diagnosis of Oral Carcinoma by the Combination of Morphological and Spectral Methods Based on Embedded Relay Lens Microscopic Hyperspectral Imaging System. J. Med. Biol. Eng. 2015, 35, 437–447. [Google Scholar] [CrossRef] [PubMed]
- Yang, X.; Xiao, X.; Wu, W.; Shen, X.; Zhou, Z.; Liu, W.; Shi, L. Cytological Study of DNA Content and Nuclear Morphometric Analysis for Aid in the Diagnosis of High-Grade Dysplasia within Oral Leukoplakia. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2017, 124, 280–285. [Google Scholar] [CrossRef]
- Jurczyszyn, K.; Kazubowska, K.; Kubasiewicz-Ross, P.; Ziółkowski, P.; Dominiak, M. Application of fractal dimension analysis and photodynamic diagnosis in the case of differentiation between lichen planus and leukoplakia: A preliminary study. Adv. Clin. Exp. Med. 2018, 27, 1729–1736. [Google Scholar] [CrossRef] [PubMed]
- Guerrero-Sánchez, Y.; Gómez García, F.; Chamorro-Petronacci, C.M.; Suárez-Peñaranda, J.M.; Pérez-Sayáns, M. Use of the Fractal Dimension to Differentiate Epithelium and Connective Tissue in Oral Leukoplakias. Cancers 2022, 14, 2697. [Google Scholar] [CrossRef] [PubMed]
- Singh, G.; Preethi, B.; Chaitanya, K.K.; Navyasree, M.; Kumar, T.G.; Kaushik, M.S. Prevalence of Oral Mucosal Lesions among Tobacco Consumers: Cross-Sectional Study. J. Pharm. Bioallied. Sci. 2023, 15, S562–S565. [Google Scholar] [CrossRef]
- Seventhy-First World Health Assembly mHealth. Use of Appropriate Digital Technologies for Public Health. Available online: https://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_R7-en.pdf (accessed on 21 November 2023).
- Contaldo, M.; Lauritano, D.; Carinci, F.; Romano, A.; Di Stasio, D.; Lajolo, C.; Della Vella, F.; Serpico, R.; Lucchese, A. Intraoral confocal microscopy of suspicious oral lesions: A prospective case series. Int. J. Dermatol. 2020, 59, 82–90. [Google Scholar] [CrossRef]
- Contaldo, M.; Di Stasio, D.; Petruzzi, M.; Serpico, R.; Lucchese, A. In vivo reflectance confocal microscopy of oral lichen planus. Int. J. Dermatol. 2019, 58, 940–945. [Google Scholar] [CrossRef]
- Jurczyszyn, K.; Kozakiewicz, M. Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser-A Prospective Study. Materials 2020, 13, 3614. [Google Scholar] [CrossRef]
- Jurczyszyn, K.; Trzeciakowski, W.; Kozakiewicz, M.; Kida, D.; Malec, K.; Karolewicz, B.; Konopka, T.; Zborowski, J. Fractal Dimension and Texture Analysis of Lesion Autofluorescence in the Evaluation of Oral Lichen Planus Treatment Effectiveness. Materials 2021, 14, 5448. [Google Scholar] [CrossRef]
- Varsha, K.S.; Krithika, C.L.; Ganesan, A.; Chandraveni, A.; Jothi, A. Pre and post treatment objective evaluation of remission in oral lichen planus using fractal analysis and comparison with visual analog (vas) and thongprasom scale-a cohort study. Int. J. Chem. Biochem. Sci. 2023, 23, 177–184. [Google Scholar]
- Chan, A.; Tuszynski, J.A. Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R. Soc. Open Sci. 2016, 3, 160558. [Google Scholar] [CrossRef] [PubMed]
- Chang, A.; Prabhala, S.; Daneshkhah, A.; Lin, J.; Subramanian, H.; Roy, H.K.; Backman, V. Early screening of colorectal cancer using feature engineering with artificial intelligence-enhanced analysis of nanoscale chromatin modifications. Res. Sq. 2023, 1–25. [Google Scholar] [CrossRef]
First Author, (Year), Country | Aims of the Study | Sample Source (Fractal Object) | Method of Fractal Dimension Calculation | Methods, Number of Cases, and Type of Lesion | Main Findings | Conclusions | |
---|---|---|---|---|---|---|---|
1 | Landini (1993), UK [39] | To quantitatively investigate nFD of normal and cancerous oral epithelial cell nuclei | Histological specimens for nuclei assessment in OC | Yardstick method for fractal dimension estimation | Total of 762 nuclei of 10 OC and normal oral mucosa, digitalized images from transmission electron microscope (×1400) | Statistically significant differences in FD of OC nuclei vs. normal cells’ nuclei | Confirmed potential use of FA for diagnosis and prognosis of malignancy. |
2 | Goutzanis (2008), Greece [32] | To evaluate the nFD in tissue specimens from patients with OC | Histological specimens for nuclei assessment in OC | Implementation of box-counting algorithm in a specially designed application (Fractalyser) | Histological sections from 48 OC and 17 healthy controls to quantify nFD using box-counting method | nFD mean values significantly increased from healthy mucosa to well differentiated and poorly differentiated OC. OC-nFD mean values were higher than normal mucosa. Patients with FD lower than the median value of the sample had statistically significant higher survival rates. | nFD was proved to be an independent prognostic factor of survival in oral cancer patients. |
3 | Goutzanis, (2009), Greece [33] | To evaluate the vascular FDs in OC to assess their potential value as factors reflecting angiogenesis | Histological specimens for vascular assessment in OC | Box-counting algorithm using the Fractalyser software | Histologic sections from 48 OC and 17 healthy controls to quantify vascular FD | OC presented statistically significant higher mean values of vascular FD compared with normal mucosa. | Vascular FD was a reliable indicator of angiogenesis in oral malignant tumors. |
4 | Margaritescu, (2010), Romania [40] | To assess the geometry of the lymphatic vessels in oral mucosa utilizing fractal analysis | Histological specimens for lymphatic vessels assessment in OC | Perimeter stepping algorithm using Image-Pro Plus software (Media Cybernetics, Inc., Bethesda, MD, USA) | Comparison between immunohistochemistry images of 20 OC with tumor-free resection margins | Comparison between contour FD values of different pathological conditions of the same area showed no statistically significant difference. | Results not statistically significant |
5 | Krishnan, (2011), India [30] | To improve the classification accuracy based on different textural features for the development of computer-assisted screening of OSF | Histological specimens for fibrosis in OSF | Modified differential box-counting with sequential algorithm | Involved 45 OSF patients and 10 healthy controls for a total of 90 images of normal oral mucosa, 42 OSF without dysplasia, and 26 OSF with dysplasia. Compared textual features obtained using FA and other textural techniques. | The combination of FD with other textural analyses led to the highest accuracy of 88.38% for classification. | Combining more than two texture measures is most effective in characterizing OSF subtypes. |
6 | Krishnan, (2012), India [20] | To analyze collagen fibers in the subepithelial connective tissue for accurate OSF screening and classification | Histological specimens for FA of fibrosis in OSF | Differential box-counting | Used 60 normal and 59 OSF images taken from histopathological samples. The segmentation of collagen fibers from histological images was performed using neural networks on color channels. Textural features were extracted from collagen areas using fractal methods like differential box-counting. | F fractal features of collagen under Gaussian transformation improves classification performance from 80.69% to 90.75%. | FA significantly improved the classification of healthy and OSF tissues. |
7 | Raja, (2012), India [21] | To investigate the usefulness of texture analysis in the OC characterization To evaluate its effectiveness in distinguishing between different grades of tumors | Computed tomography of OC | Differential box-counting | Compared 21 computed tomography images of OC patients. Two ROIs were identified: one at the site of the lesion and the other on the normal, unaffected side of the buccal mucosa. Texture analysis measures, specifically FD and gray level co-occurrence matrix (GLCM), in both ROIs. | Statistically significant differences between the mean FD and GLCM parameters of the lesion ROI and the normal ROI | FD demonstrated its usefulness in distinguishing between normal and pathological tissues, but it could not play a role in tumor grading detection. |
8 | Klatt, (2014), Germany [41] | To assess the potential of calculated fractal dimension FD of time-resolved autofluorescence in discriminating tumors from healthy tissues of the oral cavity | Histological fluorescence in OC | Fractal dimension based on time-resolved autofluorescence spectra | Histological samples from 15 OC and 22 healthy controls. After time-resolved fluorescence measurements, the FD was calculated by using an algorithm based on the non-exponential decay behavior of autofluorescence. | FD was significantly higher in OC than in healthy tissues, at 86% specificity and 100% sensitivity. | FD, based on time-resolved autofluorescence spectra, had promising potential in real-time detection of OC. |
9 | Spyridonos, (2014), Greece [34] | To quantify the morphological irregularities of the lower lip border, to validate its discriminative power in solar cheilosis diagnosis, and to provide supportive tools toward cost effective, noninvasive disease monitoring | Clinical picture of actinic cheilitis | Box-counting method and Sevcik approximation | Clinical pictures from 50 subjects were used. Two different methods for estimating FD were employed: the box-counting method (FDbc) and a method proposed by Sevcik (FDs). | FD yielded the highest accuracy in discriminating patients from controls, resulting in 98% sensitivity and 94% specificity. | FA to evaluate lip contour irregularities might be effective in distinguishing healthy lips from solar cheilosis-affected lips. |
10 | Bose, (2015), Canada [42] | To propose a method that integrates multiple histopathological features of the tumor microenvironment into a single, digital pathology-based biomarker using nFD analysis | Microarray nuclei evaluation DAPI-stained images of tissue microarray (TMA) cores | Box-counting | A total of 107 consecutive OC patients were classified using nFD scores of nuclei stained with DAPI from TMA. | High nFD was significantly associated with pT-stage and RT. High nFD of the total tumor microenvironment was significantly associated with improved disease-specific survival. High nFD was significantly associated with high tumor proliferation and lymphatic invasion. | nFD analysis integrates known prognostic factors from the tumor microenvironment, such as proliferation and immune infiltration, into a single digital pathology-based biomarker. |
11 | Lucchese, (2015), Italy [36] | To assess local vascular architecture in atrophic-erosive OLP | OPL capillaroscopy | Box-counting | Used 31 OLP patients and 32 healthy controls. The images captured with capillaroscopy were converted to 8-bit grayscale, and the box-counting method was used to assess the FD. | Statistically significant differences in the FD of vessels’ density in OLP and healthy controls | Microvessel density analysis could be used as a parameter in determining potential malignant progression of OLP lesions, but more studies are needed. |
12 | Mincione, (2015), Italy [37] | To investigate FD as an OC prognostic tool by correlating FD values with clinicopathological features and survival of OC patients | Immunohistochemistry of podoplanin in OC | Box-counting | Used 64 OC and 10 healthy controls. Postproduction analysis of the specimen images was performed, and the box-counting method was used to assess FD. Podoplanin expression in tumor-free resection margins of OC | The mean FD values difference was statistically significant between the control and test groups. Increasing value of FD statistically correlated with different stages, grades, and survival of OC. | FD correlates with OC histological grade and stage and can be used for prognosticating OC survival. |
13 | Ou-Yang, (2015), Taiwan [43] | To develop a combination of textural and spectral methods for diagnosing OC | Histological images from an inverted microscope | Morphology-based fractal dimension method for tissue discrimination | Total of 34 OC and 34 patient-related healthy mucosa | FD had 90% sensitivity and 88% specificity in distinguishing among OC and healthy mucosa. | FD was effective in detecting OC in biopsies with high sensitivity and specificity. |
14 | Pandey, (2015), India [22] | To evaluate the FD of OL compared with normal oral mucosa and the changes during and after treatments | Clinical pictures of OL | Box-counting | Clinical pictures of 50 OL and 50 normal mucosa considered for FA after postproduction ROI selection. | The difference between the two groups was statistically significant. The difference in FDs between pretreated and post-treated lesions was observed and was suggestive of decreased FD. | FA can be an effective, economical, and noninvasive diagnostic and prognostic tool for OL. |
15 | Spirydonos, (2015), Greece [35] | To determine robust macro-morphological descriptors of the vermilion border from non-standardized digital photographs | Clinical picture of actinic cheilitis | Box-counting method and Sevcik approximation | Clinical images from 75 AC and 75 healthy controls. Lip borders quantified on the basis of the extent of vermilion retraction and the degree of border irregularity employing fractal features. | The different FD values were related with individual variabilities other than the status (AC vs. healthy lips). | The proposed method opens new perspectives toward a cost-effective, noninvasive monitoring of AC. |
16 | Yinti, (2015), India [23] | To assess nuclear morphologic complexity with nFD obtained from computer-aided image analysis and correlate the fractal dimension with clinical features | Histological specimens for nuclei assessment in OC | Sarkar box-counting method | Histopathological and postproduction analysis of 14 OC and 6 healthy controls. After hematoxylin and eosin for histopathological assessment, Feulgen staining was performed to evaluate nuclear complexity. Using Adobe Adobe PhotoShop CS and ImageJ software 1.43u (Wayne Rasband, National Institutes of Health, Bethesda, USA), postproduction analysis was performed. | Higher mean nFD was observed in the OC group compared with the control group. Significant difference in the average value of nFD between the four stages of the disease. Patients with FD value ≤ 1.71 showed a higher survival period of 72 months, while patients with FD > 1.71 showed a lower survival period of 36 months. | nFD seemed a reliable diagnostic tool that need standardization to be validated, but the data collected suggest the possible use of FD also as a prognostic factor. |
17 | Phulari, (2016), India [24] | To compare the morphometric complexity using nFD in normal, epithelial dysplasia, and OC cases and to verify the differences among the various histological grades of dysplasia and OC | Histological specimens for assessment for distinguishing OC and various degrees of dysplasia | Box-counting | Used 70 histological samples of normal mucosa, mild dysplasia, moderate dysplasia, severe dysplasia, well-differentiated OC, moderately differentiated OC, and poorly differentiated OC. The images were analyzed using ImageJ and the box-counting algorithm. | Progressive increase in mean FD from healthy mucosa to poorly differentiated OC. | FA could be a reliable tool for distinguishing the normal, dysplastic, and neoplastic tissues. |
18 | Das, (2017), India [25] | To develop a microscopic image analytics approach for automated recognition of mitotic cells and its count for assisting pathological evaluation of OC | Histological specimens for assessment of mitotic cells | Modified differential box-counting method | Five histological slides for each grade, fifteen slides, and ten images for every region of interest. FD was calculated using the box-counting method. | Found 89% precision in mitotic cell segmentation. | The proposed methodology was effective for mitotic cell detection in OC histopathological images. |
19 | Yang, (2017), China [44] | To quantitatively examine the DNA content and nuclear morphometry status of OL and investigate their association with the degree of dysplasia | Cytology study to assess DNA content amount, nuclear shape, area, radius, intensity, sphericity, entropy, and nFD | Not specified | Cytobrush samples from 70 OLs, before the scalpel biopsy, were stained with Feulgen-thionin. | A total of 48.6% of the OLs had a DNA content abnormality; positive correlation was observed between the degree of oral dysplasia and DNA content status. | DNA content and nuclear morphometric status using cytobrush biopsy with image cytometry contribute to diagnosing high-grade dysplasia within OL. |
20 | Daddazio, (2018), Italy [38] | To consider a possible correlation between the intensity of expression of osteopontin and grading in OC To correlate the increase in FD and osteopontin | Histological OC | Box-counting | Used 64 OC and 14 healthy controls and immunohistochemical stain to identify and localize osteopontin. Postproduction analysis was performed using ImageJ and the box-counting method. | Statistically significant differences found in the FD values between the test group and controls. Correlation between FD and OPN expression was visually more considerable when divided by tumor grading, especially in the G3 group. | The study suggests a potential correlation between osteopontin expression, FD values, and OC grading. Combining these factors may enhance diagnostic accuracy and prognostic evaluation. |
21 | Jurczyszyn, (2018), Poland [45] | To distinguish OL and OLP using FA in a classical examination with white light and PDD | Clinical photos and photodynamic diagnosis photos of OL and OLP | Modified box-counting | In 41 patients with OL or OLP, photodynamic therapy (PDT) with 5-ALA was administered, and FA was conducted using the Fractalyse program to evaluate the efficacy of PDT in treating oral lesions. | No significant differences were observed between the FDs of OL and OLP. | Variations within groups were noted, although its utility in distinguishing between LP and leukoplakia without histopathological examination remains inconclusive. |
22 | Iqbal, (2020), India [26] | To assess the efficacy of FA in detecting the malignancy potential of OL | Clinical photo after toluidine blue staining of OL | Box-counting | In 121 OL and healthy controls, digital images of normal mucosa and lesions were taken before and after staining with toluidine blue. Postproduction analysis performed using the box-counting method. | FD values showed a significant difference between dysplastic and nondysplastic cases. FD values based on age and the type of tobacco product used indicated an increasing trend with advancing age. Surti/khaini abusers showed a significant difference in FD values. The correlation of FD values with age and the duration of smoking and smokeless tobacco was highly significant. | FD analysis could be used as a noninvasive, cost-effective diagnostic tool for the early detection of malignant conversion. |
23 | Nawn, (2021), India [27] | To explore and analyze oral differences in FD among normal mucosa, OSF, OSF with dysplasia, OL, and OC | Histopathological images | Not specified | Histological sections of healthy mucosa, OSF, OSF with dysplasia, OL, and OC were considered for tissue grading and FA. | Discriminative multifractal signatures for healthy and pathological tissues | FA was useful to distinguish alterations in the singularity spectrum width across healthy, pre-cancerous, and cancerous tissues. |
24 | Sharma, (2021), India [28] | To understand the crystallization patterns in saliva and their relation to oral potentially malignant disorders in male patients | Salivary specimens for assessment of crystallization pattern in OSF, OL, and OC | Not specified | Dried salivary films from patients with OSF, OL, and OC were examined under a stereo-zoom microscope to select ROIs for fern structure analysis. | Significant differences in FD among normal individuals and those with OSF, OL, and OC | Saliva could serve as a potential imaging biomarker for the early-stage, noninvasive diagnosis of OPMDs and OC. |
25 | Guerrero-Sánchez, (2022), Spain [46] | To assess dysplasia | Histological specimens for assessment of FD of in OL | Modified box-counting | A total of 29 OL and 10 normal oral mucosa biopsies were analyzed using FA for the epithelial and the connective layer. | In the OL group, the FD median value was higher compared with the control group, with statistically significant differences. Significant differences were observed between the non-dysplasia vs. high-grade and low-grade vs. high-grade groups. | FD is an effective tool for diagnosing OL when evaluating the epithelial layer. |
26 | Rahman, (2022), India [31] | To evaluate differences in nFD values of epithelial cells of normal tissue, fibroepithelial hyperplasia, verrucous carcinoma, and OSCC. Also, the correlation between these features and the cervical lymph node metastasis was assessed. | Histological specimens for assessment of epithelial cells from fibroepithelial hyperplasia, verrucous carcinoma, and OSCC | Box-counting | Photo of samples underwent postproduction analysis with Image J. All the clinical features were then compared with the image analysis results. | Significant difference between the mean nFD of healthy cells and malignant epithelial cells. nFD and grading together demonstrated significant predictive potential for lymph node metastasis. | nFD combined with grading may predict lymph node metastasis. |
27 | Santolia, (2022), India [29] | To assess the fractal dimension (FD) and radiomorphometric indices (RMIs) in the mandible from orthopantomographic radiographs in patients with oral lesions | Radiological images in patients with tobacco and areca-nut-associated oral lesions | Box counting method by White and Rudolph | FD and radiomorphometric indices were assessed, along with participant habits, BMI, and statistical analyses. | Mean FD was significantly reduced in patients with oral lesions compared with controls. FD and RMI values were significantly altered in patients with oral lesions associated with tobacco and areca nut habits. | These imaging parameters could potentially serve as indicators or markers for assessing oral health in individuals with specific tobacco and areca nut habits in the North Indian population. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Contaldo, M.; Di Spirito, F.; Di Palo, M.P.; Amato, A.; Fiori, F.; Serpico, R. Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review. Appl. Sci. 2024, 14, 777. https://doi.org/10.3390/app14020777
Contaldo M, Di Spirito F, Di Palo MP, Amato A, Fiori F, Serpico R. Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review. Applied Sciences. 2024; 14(2):777. https://doi.org/10.3390/app14020777
Chicago/Turabian StyleContaldo, Maria, Federica Di Spirito, Maria Pia Di Palo, Alessandra Amato, Fausto Fiori, and Rosario Serpico. 2024. "Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review" Applied Sciences 14, no. 2: 777. https://doi.org/10.3390/app14020777
APA StyleContaldo, M., Di Spirito, F., Di Palo, M. P., Amato, A., Fiori, F., & Serpico, R. (2024). Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review. Applied Sciences, 14(2), 777. https://doi.org/10.3390/app14020777