Tumorigenesis: it takes a village.
Nat. Rev. Cancer. 2015; 15: 473-483
Spatial and temporal cancer evolution: causes and consequences of tumour diversity.
Clin. Med. 2014; 14: s33-s37
Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine.
Genome Biol. 2014; 15: 453
Spatial heterogeneity in the tumor microenvironment.
Cold Spring Harb. Perspect. Med. 2016; 6a026583
Tumour heterogeneity in the clinic.
Nature. 2013; 501: 355-364
Biological and therapeutic impact of intratumor heterogeneity in cancer evolution.
Cancer Cell. 2015; 27: 15-26
Addressing intra-tumoral heterogeneity and therapy resistance.
Oncotarget. 2016; 7: 72322-72342
Preparing viable single cells from human tissue and tumors for cytomic analysis.
Curr. Protoc. Mol. Biol. 2017; 118: 25C.1.1-25C.1.23
A comprehensive guide for performing sample preparation and top-down protein analysis.
Proteomes. 2017; 5: 1-31
Recent applications of magnetic solid-phase extraction for sample preparation.
Chromatographia. 2019; 82: 1251-1274
Microfluidic sample preparation for single cell analysis.
Anal. Chem. 2016; 88: 354-380
The sequence of sequencers: the history of sequencing DNA.
Genomics. 2016; 107: 1-8
Opportunities and challenges in long-read sequencing data analysis.
Genome Biol. 2020; 21: 30
Current practices and guidelines for clinical next-generation sequencing oncology testing.
Cancer Biol. Med. 2016; 13: 3-11
Next-generation sequencing and its clinical application.
Cancer Biol. Med. 2019; 16: 4-10
Beyond mass spectrometry, the next step in proteomics.
Sci. Adv. 2020; 6: 1-17
Review of three-dimensional liquid chromatography platforms for bottom-up proteomics.
Int. J. Mol. Sci. 2020; 23: 1524
Why use signal-to-noise as a measure of MS Performance when it is often meaningless?.
Curr. Topics Mass Spectrom. 2011; 9: 28-33
Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues.
Nat. Med. 2001; 7: 493-496
Real-time shape approximation and fingerprinting of single proteins using a nanopore.
Nat. Nanotechnol. 2017; 12: 360-367
Emerging applications of flow cytometry in solid tumor biology.
Methods. 2012; 57: 359-367
Design and analysis of single-cell sequencing experiments.
Cell. 2015; 163: 799-810
Microfluidic cell sorting: a review of the advances in the separation of cells from debulking to rare cell isolation.
Lab Chip. 2015; 15: 1230-1249
A review on deterministic lateral displacement for particle separation and detection.
Nano-Micro Lett. 2019; 11: 77
A flow through device for simultaneous dielectrophoretic cell trapping and AC electroporation.
Sci. Rep. 2019; 9: 1-11
Microfluidic sorting and multimodal typing of cancer cells in self-assembled magnetic arrays.
Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 14524-14529
Microfluidic, label-free enrichment of prostate cancer cells in blood based on acoustophoresis.
Anal. Chem. 2012; 84: 7954-7962
Microfluidic integrated optoelectronic tweezers for single-cell preparation and analysis.
Lab Chip. 2013; 13: 3721-3727
Microfluidic single cell real-time PCR for comparative analysis of gene expression patterns.
Nat. Protoc. 2012; 7: 829-838
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Cell. 2015; 161: 1202-1214
Massively parallel digital transcriptional profiling of single cells.
Nat. Commun. 2017; 8: 14049
Comparison of whole genome amplification techniques for human single cell exome sequencing.
PLoS ONE. 2017; 12e0171566
Tumour heterogeneity and metastasis at single-cell resolution.
Nat. Cell Biol. 2018; 20: 1349-1360
High-throughput microfluidic imaging flow cytometry.
Curr. Opin. Biotechnol. 2019; 55: 36-43
Droplet microfluidics: fundamentals and its advanced applications.
RSC Adv. 2020; 10: 27560-27574
Dynamic analysis of immune and cancer cell interactions at single cell level in microfluidic droplets.
Biomicrofluidics. 2016; 10: 1-12
Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.
Anal. Chem. 2009; 81: 6813-6822
A single-cell atlas of the tumor and immune ecosystem of human breast cancer.
Cell. 2019; 177: 1330-1345
Fully cooked FISH.
Nat. Rev. Genet. 2007; 8: S6
Spatially resolved, highly multiplexed RNA profiling in single cells.
Science. 2015; 348: 1360-1363
Direct multiplexed measurement of gene expression with color-coded probe pairs.
Nat. Biotechnol. 2008; 26: 317-325
Highly multiplexed subcellular RNA sequencing in situ.
Science. 2014; 343: 1360-1363
In situ genome sequencing resolves DNA sequence and structure in intact biological samples.
Science. 2021; 371eaay3446
Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution.
Science. 2019; 363: 1463-1467
Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues.
Nat. Biotechnol. 2019; 37: 1080-1090
Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front.
Cell. 2020; 182: 1341-1359
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.
Nat. Methods. 2014; 11: 417-422
Multiplexed ion beam imaging of human breast tumors.
Nat. Med. 2014; 20: 436-442
Laser-capture microdissection.
Nat. Protoc. 2006; 1: 586-603
Laser-assisted microdissection in translational research.
Appl. Immunohistochem. Mol. Morphol. 2012; 21: 31-47
Laser capture microdissection: big data from small samples.
Histol. Histopathol. 2015; 30: 1255-1269
The spatial and genomic hierarchy of tumor ecosystems revealed by single-cell technologies.
Trends Cancer. 2019; 5: 411-425
A mill based instrument and software system for dissecting slide-mounted tissue that provides digital guidance and documentation.
BMC Clin. Pathol. 2013; 13: 1-12
Micro-immunohistochemistry using a microfluidic probe.
Lab Chip. 2012; 12: 1040-1043
Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues.
Nat. Biomed. 2019; 3: 478-490
Rapid micro fluorescence in situ hybridization in tissue sections.
Biomicrofluidics. 2018; 12042212
Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity.
Nucleic Acids Res. 2020; 48e17
Mapping spatial genetic landscapes in tissue sections through microscale integration of sampling methodology into genomic workflows.
Small. 2021; 17: 2007901
Spatially resolved genetic analysis of tissue sections enabled by microscale flow confinement retrieval and isotachophoretic purification.
Angew. Chem. 2019; 58: 15259-15262
Selective local lysis and sampling of live cells for nucleic acid analysis using a microfluidic probe.
Sci. Rep. 2016; 6: 1-10
Fundamentals of Medical Imaging.
2nd edn. Cambridge University Press, 2009
Radiopharmaceutical therapy in cancer: clinical advances and challenges.
Nat. Rev. Drug Discov. 2020; 19: 589-608
Biomedical imaging: principles, technologies, clinical aspects, contrast agents, limitations and future trends in nanomedicines.
Pharm. Res. 2019; 36: 78
New technologies for human cancer imaging.
J. Clin. Oncol. 2008; 26: 4012-4021
Quantitative multimodality imaging in cancer research and therapy.
Nat. Rev. Clin. Oncol. 2014; 11: 670-680
Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples.
Curr. Med. Imaging Rev. 2007; 3: 91-107
Scaling single-cell genomics from phenomenology to mechanism.
Nature. 2017; 541: 331-338
Unmasking tumor heterogeneity and clonal evolution by single-cell analysis.
J. Cancer Metastasis Treat. 2018; 4: 47
Integrative single-cell analysis.
Nat. Rev. Genet. 2019; 20: 257-272
SABER enables amplified and multiplexed imaging of RNA and DNA in cells and tissues.
Nat. Methods. 2019; 16: 533-544
Highly multiplexed simultaneous detection of RNAs and proteins in single cells.
Nat. Methods. 2016; 13: 269-275
Single-cell metabolomics: analytical and biological perspectives.
Science. 2013; 342: 1243259
SpaceM reveals metabolic states of single cells.
Nat. Methods. 2021; 18: 799-805
Clinical and regulatory aspects of companion diagnostic development in oncology.
Clin. Pharmacol. Ther. 2018; 103: 999-1008
CRISPR-based diagnostics. Nat.
Biomed. Eng. 2021; 5: 643-656
Liquid biopsy enters the clinic — implementation issues and future challenges.
Nat. Rev. Clin. Oncol. 2021; 18: 297-312
Radiomics: the facts and the challenges of image analysis.
Eur. Radiol. Exp. 2018; 2: 36
Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer.
Eur. Radiol. 2021; 31: 3765-3772
The cell biologist’s guide to super-resolution microscopy.
J. Cell Sci. 2020; 133jcs240713
Super-resolution microscopy reveals ultra-low CD19 expression on myeloma cells that triggers elimination by CD19 CAR-T.
Nat. Commun. 2019; 10: 3137
Harnessing non-destructive 3D pathology.
Nat. Biomed. Eng. 2021; 5: 203-218
MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma.
Oral Oncol. 2013; 49: 211-215
High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma.
Cancer. 2013; 119: 3034-3042
Prognostic significance of mutant-allele tumor heterogeneity in uterine corpus endometrial carcinoma.
Ann. Transl. Med. 2020; 8: 339
Clinical relevance of mutant-allele tumor heterogeneity and lung adenocarcinoma.
Ann. Transl. Med. 2019; 7: 432
Clinical and molecular relevance of mutant-allele tumor heterogeneity in breast cancer.
Breast Cancer Res. Treat. 2017; 162: 39-48
Gender-related prognostic value and genomic pattern of intra-tumor heterogeneity in colorectal cancer.
Carcinogenesis. 2017; 38: 837-846
Intratumor heterogeneity inferred from targeted deep sequencing as a prognostic indicator.
Sci. Rep. 2019; 9: 4542
Principles of reconstructing the subclonal architecture of cancers.
Cold Spring Harb. Perspect. Med. 2017; 7a026625
Cancer evolution: mathematical models and computational inference.
Syst. Biol. 2015; 64: e1-e25
Resolving genetic heterogeneity in cancer.
Nat. Rev. Genet. 2019; 20: 404-416
EXPANDS: expanding ploidy and allele frequency on nested subpopulations.
Bioinformatics. 2014; 30: 50-60
PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors.
Genome Biol. 2015; 16: 35
Absolute quantification of somatic DNA alterations in human cancer.
Nat. Biotechnol. 2012; 30: 413-421
Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability.
PLoS Genet. 2018; 14e1007669
Phylogenetic quantification of intra-tumour heterogeneity.
PLoS Comput. Biol. 2014; 10e1003535
Phylogenetic quantification of intratumor heterogeneity.
Cold Spring Harb. Perspect. Med. 2018; 8a028316
Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis.
PLoS Med. 2015; 12e1001789
Systematic pan-cancer analysis of tumour purity.
Nat. Commun. 2015; 6: 8971
Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants.
Bioinformatics. 2018; 34: 2004-2011
Ploidy and purity adjusted DNA allele specific analysis using CLONETv2.
Curr. Protoc. Bioinformatics. 2019; 67e81
Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power.
Sci. Rep. 2018; 8: 11445
Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data.
PLoS ONE. 2019; 14e0224143
Predicting clone genotypes from tumor bulk sequencing of multiple samples.
Bioinformatics. 2018; 34: 4017-4026
Eleven grand challenges in single-cell data science.
Genome Biol. 2020; 21: 31
Cellular heterogeneity and molecular evolution in cancer.
Annu. Rev. Pathol. 2013; 8: 277-302
Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype.
J. Clin. Invest. 2010; 120: 636-644
Genetic and phenotypic diversity in breast tumor metastases.
Cancer Res. 2014; 74: 1338-1348
In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer.
Nat. Genet. 2015; 47: 1212-1219
Diversity index as a novel prognostic factor in breast cancer.
Oncotarget. 2017; 8: 97114-97126
Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer.
Science. 2013; 339: 543-548
Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance.
Mol. Cell. 2014; 54: 716-727
Evolution of DNA methylation is linked to genetic aberrations in chronic lymphocytic leukemia.
Cancer Discov. 2014; 4: 348-361
A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations.
Cell. 2010; 141: 69-80
Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours.
Nat. Rev. Genet. 2009; 10: 336-342
An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles.
Commun. Biol. 2020; 3: 505
Measuring intratumor heterogeneity by network entropy using RNA-seq data.
Sci. Rep. 2016; 6srep37767
SpliceHetero: an information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq.
PLoS ONE. 2019; 14e0223520
Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.
BMC Syst. Biol. 2016; 10: 65
Single-cell transcriptomic analysis of tumor heterogeneity.
Trends Cancer. 2018; 4: 264-268
Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer.
Cancer Cell. 2019; 36: 418-430
Heterogeneity mapping of protein expression in tumors using quantitative immunofluorescence.
J. Vis. Exp. 2011; 5e3334
Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue.
Lab. Investig. 2012; 92: 1342-1357
Single-cell heterogeneity in ductal carcinoma in situ of breast.
Mod. Pathol. 2018; 31: 406-417
Platform for quantitative evaluation of spatial intratumoral heterogeneity in multiplexed fluorescence images.
Cancer Res. 2017; 77: e71-e74
Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers.
J. Pathol. Inform. 2016; 7: 47
Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures.
PLoS ONE. 2017; 12e0188878
From single cells to deep phenotypes in cancer.
Nat. Biotechnol. 2012; 30: 639-647
Unraveling cell populations in tumors by single-cell mass cytometry.
Curr. Opin. Biotechnol. 2015; 31: 122-129
The single-cell pathology landscape of breast cancer.
Nature. 2020; 578: 615-620
CellCycleTRACER accounts for cell cycle and volume in mass cytometry data.
Nat. Commun. 2018; 9: 632
Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology.
Lab. Investig. 2015; 95: 377-384
Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes.
PLoS ONE. 2010; 5e12420
Quantitative characterization of cd8+ t cell clustering and spatial heterogeneity in solid tumors.
Front. Oncol. 2019; 8: 649
Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer.
J. R. Soc. Interface. 2015; 12: 20141153
Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer.
Mod. Pathol. 2015; 28: 766-777
Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score.
Sci. Rep. 2016; 6: 36231
A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging.
Cell. 2018; 174: 1373-1387
Deconstructing tumor heterogeneity: the stromal perspective.
Oncotarget. 2020; 11: 3621-3632
Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection.
Sci. Rep. 2018; 8: 7226
Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy.
Radiology. 2014; 270: 141-148
Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.
Radiology. 2013; 266: 177-184
Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.
PLoS ONE. 2017; 12e0188022
Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment.
PLoS ONE. 2016; 11e0161278
Quantification of structural heterogeneity using fractal analysis of contrast-enhanced CT image to predict survival in gastric cancer patients.
Dig. Dis. Sci. 2020; 66: 2069-2074
Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis.
Eur. Radiol. 2009; 19: 1358-1365
Fractal analysis of contrast-enhanced CT images for preoperative prediction of malignant potential of gastrointestinal stromal tumor.
Abdom. Radiol. 2018; 43: 2659-2664
Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib.
Dig. Dis. Sci. 2014; 59: 1996-2003
Combining molecular and imaging metrics in cancer: radiogenomics.
Insights Imaging. 2020; 11: 1
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Nat. Commun. 2014; 5: 4006
Decoding global gene expression programs in liver cancer by noninvasive imaging.
Nat. Biotechnol. 2007; 25: 675-680
Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features.
J. Med. Imaging (Bellingham). 2015; 2041009
Understanding the standardized uptake value, its methods, and implications for usage.
J. Nucl. Med. 2004; 45: 1431-1434
Dependence of FDG uptake on tumor microenvironment.
Int. J. Radiat. Oncol. Biol. Phys. 2005; 62: 545-553
The complexity and fractal geometry of nuclear medicine images.
Mol. Imaging Biol. 2019; 21: 401-409
SUV: standard uptake or silly useless value?.
J. Nucl. Med. 1995; 36: 1836-1839
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes.
Pattern Recogn. 2009; 42: 1162-1171
Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.
J. Nucl. Med. 2011; 52: 369-378
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.
Eur. Radiol. 2012; 22: 796-802
Prognostic impact of intratumoral heterogeneity based on fractal geometry analysis in operated NSCLC patients.
Mol. Imaging Biol. 2019; 21: 965-972
FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules.
Eur. J. Radiol. 2014; 83: 715-719
Investigating the histopathologic correlates of 18F-FDG PET heterogeneity in non-small-cell lung cancer.
Nucl. Med. Commun. 2018; 39: 1197-1206
Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes.
Radiology. 2016; 282: 665-675
Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.
J. Am. Med. Inform. Assoc. 2013; 20: 1059-1066
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
Breast Cancer Res. 2017; 19: 57
Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI.
Magn. Reson. Imaging. 2016; 34: 809-819
Non-Hodgkin lymphoma response evaluation with MRI texture classification.
J. Exp. Clin. Cancer Res. 2009; 28: 87
Relationship between glioblastoma heterogeneity and survival time: an MR imaging texture analysis.
Am. J. Neuroradiol. 2017; 38: 1695-1701
Tumor heterogeneity in oral and oropharyngeal squamous cell carcinoma assessed by texture analysis of CT and conventional MRI: a potential marker of overall survival.
Acta Radiol. 2019; 60: 1273-1280
Relationship between necrotic patterns in glioblastoma and patient survival: fractal dimension and lacunarity analyses using magnetic resonance imaging.
Sci. Rep. 2017; 7: 8302
Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response?.
Phys. Med. Biol. 2011; 56: 1601-1616
DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6.
Br. J. Cancer. 2011; 105: 139-145
Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation.
J. Digit. Imaging. 2010; 23: 342-357
Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.
Med. Image Comput. Assist. Interv. 2013; 16: 295-302
On some misconceptions about tumor heterogeneity quantification.
Eur. J. Nucl. Med. Mol. Imaging. 2013; 40: 1292-1294
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?.
Insights Imaging. 2012; 3: 573-589
Reproducibility of radiomics for deciphering tumor phenotype with imaging.
Sci. Rep. 2016; 6: 23428
Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors.
Cell Rep. 2019; 29: 2164-2174
Geospatial immune variability illuminates differential evolution of lung adenocarcinoma.
Nat. Med. 2020; 26: 1054-1062
Hierarchical graph representations in digital pathology.
Med. Image Anal. 2021; 75102264
Deep learning: new computational modelling techniques for genomics.
Nat. Rev. Genet. 2019; 20: 389-403
Quantifying explainers of graph neural networks in computational pathology.
in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021: 8106-8116
MRI-based classification of brain tumor type and grade using SVM-RFE.
in: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009: 1035-1038
Biologically relevant heterogeneity: metrics and practical insights.
SLAS Discov. 2017; 22: 213-237
Measuring Biological Diversity.
John Wiley & Sons, 2013
A mathematical theory of communication.
Bell Syst. Tech. J. 1948; 27: 379-423
Modelling spatial patterns.
J. R. Stat. Soc. Ser. B Methodol. 1977; 39: 172-192
Comments on Ripley’s paper.
J. R. Stat. Soc. Ser. B. 1977; 39: 193-195
histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data.
Nat. Methods. 2017; 14: 873
CT texture analysis: definitions, applications, biologic correlates, and challenges.
RadioGraphics. 2017; 37: 1483-1503
Textural features for image classification.
IEEE Trans. Syst. Man. Cybern. 1973; SMC-3: 610-621
Statistical and structural approaches to texture.
Proc. IEEE. 1979; 67: 786-804
Textural features corresponding to textural properties.
IEEE Trans. Syst. Man. Cybern. 1989; 19: 1264-1274
Texture analysis using gray level run lengths.
Comput. Graph. Image Process. 1975; 4: 172-179
Surface roughness classification for castings.
Pattern Recogn. 1999; 32: 389-405
Content-based image retrieval using Gabor texture features.
in: First IEEE Pacific-Rim Conference on Multimedia. IEEE, 2000: 13-15
Fractals. Form, Chance and Dimension.
W.H. Freeman, 1977
An efficient differential box-counting approach to compute fractal dimension of image.
IEEE Trans. Syst. Man. Cybern. 1994; 24: 115-120
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