Scoring of collagen organization in healthy and diseased human dermis by multiphoton microscopy

Cicchi, Riccardo; Kapsokalyvas, Dimitrios; Giorgi, Vincenzo De; Maio, Vincenza; Wiechen, Annelies Van; Massi, Daniela; Lotti, Torello; Pavone, Francesco S.

doi:10.1002/jbio.200910062

Cited by 202 publications

(209 citation statements)

References 39 publications

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“…Additional quantitative evaluation of the rejuvenating process was performed by means of GLCM method, which is an image pattern analysis methodology already used to determine collagen modifications in dermal pathologies such as keloid or hypertrophic scars [50]. While the SAAID index method is useful to assess the ratio between collagen and elastic content, GLCM-based analysis allows to investigate the level of organization of collagen fibres inside the dermis and their modifications.…”

Section: Glcm Scoring Methodsmentioning

confidence: 99%

“…GLCM matrix, GLCM correlation and ASM were calculated versus neighbour index using a customdeveloped Matlab routine that sum horizontal and vertical GLCM matrixes. Additional details of the method used can be found elsewhere [28,50].…”

Section: Image Acquisition Processing and Analysismentioning

confidence: 99%

“…This combined technique has been widely used for imaging and characterizing human skin dermis. In particular, it has been used for monitoring collagen alteration in dermal disorders [50] or at the tumour-stroma interface [51][52][53], as well as to monitor skin aging by measuring the collagen/elastic fibres content [54][55][56].…”

Section: Introductionmentioning

confidence: 99%

“…The age-dependent effectiveness of the treatment was confirmed by a quantitative spectral analysis, based on the second-harmonic to autofluorescence aging index of dermis (SAAID) [54], which is a recognized scoring method for skin aging assessment by means of non-linear microscopy. Additional characterization of collagen organization was tested by pattern analysis of SHG images using grey-level co-occurrence matrix (GLCM), a well-established method for scoring collagen organization in skin [50]. The method presented here represents a promising tool to be used for the follow-up of a broad range of collagen-targeted therapies in dermatology, including pharmacological, cosmeceutical, and laser-based treatments.…”

Section: Introductionmentioning

confidence: 99%

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In vivo non‐invasive monitoring of collagen remodelling by two‐photon microscopy after micro‐ablative fractional laser resurfacing

Cicchi

Kapsokalyvas²,

Troiano

et al. 2013

Journal of Biophotonics

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Journal of BIOPHOTONICSNon-linear optical microscopy is becoming popular as a non-invasive in vivo imaging modality in dermatology. In this study, combined TPF and SHG microscopy were used to monitor collagen remodelling in vivo after microablative fractional laser resurfacing. Papillary dermis of living subjects, covering a wide age range, was imaged immediately before and forty days after treatment. A qualitative visual examination of acquired images demonstrated an age-dependent remodelling effect on collagen. Additional quantitative analysis of new collagen production was performed by means of two image analysis methods. A higher increase in SHG to TPF ratio, corresponding to a stronger treatment effectiveness, was found in older subjects, whereas the effect was found to be negligible in young, and minimal in middle age subjects. Analysis of collagen images also showed a dependence of the treatment effectiveness with age but with controversial results. While the diagnostic potential of in vivo multiphoton microscopy has already been demonstrated for skin cancer and other skin diseases, here we first successfully explore its potential use for a non-invasive follow-up of a laser-based treatment.Three-dimensional projection of a stack of 20 SHG images acquired within dermis of a healthy subject. Volume shown: 400 Â 400 Â 100 mm 3 .

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Section: Glcm Scoring Methodsmentioning

confidence: 99%

Section: Image Acquisition Processing and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In vivo non‐invasive monitoring of collagen remodelling by two‐photon microscopy after micro‐ablative fractional laser resurfacing

Cicchi

Kapsokalyvas²,

Troiano

et al. 2013

Journal of Biophotonics

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“…These structures stabilize the surrounding tissue and, hence, fulfill a structurally supporting task within the tissue. Generally, during the development of various diseases collagen structures underlie alterations which are readily detectable by means of SHG imaging [19,20]. With respect to the formation of cancer, early stage malignancies are typically surrounded by a capsule of collagen rich tissue or sits on top of the basal membrane, a natural boundary, which prevents the cancer from spreading into neighboring tissue.…”

Section: Biophotonicsmentioning

confidence: 99%

Multimodal imaging to study the morphochemistry of basal cell carcinoma

Vogler

Meyer

Akimov

et al. 2010

Journal of Biophotonics

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Basal cell carcinoma is the most abundant malignant neoplasm in humans, the pathology of which is characterized by an abnormal proliferation of basal cells. Basal cell carcinoma can show a variety of different morphologies, which are based on different cellular biology. Furthermore, the carcinoma often grows invisibly to the eye imbedded in the surrounding skin. Therefore, in some cases its clinical detection is challenging. Thus, our work aims at establishing an unsupervised tissue classification method based on multimodal imaging and the application of chemometrics to discriminate basal cell carcinoma from non‐diseased tissue. A case study applying multimodal imaging to ex‐vivo sections of basal cell carcinoma is presented. In doing so, we apply a combination of various linear and non‐linear imaging modalities, i.e. fluorescence, Raman and second‐harmonic generation microscopy, to study the morphochemistry of basal cell carcinoma. The joint information content obtained by such multimodal approach in studying various aspects of the malignant tissue alterations associated with basal cell carcinoma is discussed. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

Chen

Jiang

et al. 2019

JAMA Surg

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IMPORTANCE Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC. OBJECTIVE To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC. DESIGN, SETTING, AND PARTICIPANTS This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated. MAIN OUTCOMES AND MEASURES The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM. RESULTS In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found. CONCLUSIONS AND RELEVANCE This study's findings suggest th...

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Scoring of collagen organization in healthy and diseased human dermis by multiphoton microscopy

Cited by 202 publications

References 39 publications

In vivo non‐invasive monitoring of collagen remodelling by two‐photon microscopy after micro‐ablative fractional laser resurfacing

In vivo non‐invasive monitoring of collagen remodelling by two‐photon microscopy after micro‐ablative fractional laser resurfacing

Multimodal imaging to study the morphochemistry of basal cell carcinoma

Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

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