Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues.

The differences in the biochemistry of normal and cancerous tissue could be better exploited by Raman spectroscopy when the spectral information from normal tissue is subtracted from the abnormal tissues. In this study, we evaluated the use of the normal-subtracted spectra to evidence the biochemical differences in the pre-cancerous and cancerous skin tissues compared with normal skin, and to discriminate the groups with altered tissues with respect to the normal sites. Raman spectra from skin tissues [normal (Normal), benign (dermatitis-BEN), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and actinic keratosis (KER)] were obtained in vivo (Silveira et al., 2015, doi: https://doi.org/10.1002/lsm.22318) and used to develop the spectral model. The mean spectrum of the normal sites (circumjacent to each lesion) from each subject was calculated and subtracted from each individual spectrum of that particular subject independently of the group (Normal, BEN, BCC, SCC, KERAT). The mean spectra of each altered group and the mean spectra of the differences were firstly evaluated in terms of biochemical contribution or differentiation comparing the normal site. Then, the normal-subtracted spectra were submitted to discriminant models based on partial least squares and principal components regression (PLS-DA and PCR-DA), and the discrimination were compared with the model using non-subtracted spectra. Results showed that the peaks of nucleic acids, lipids (triolein) and proteins (elastin and collagens I, III, and IV) were significantly different in the lesions, higher for the pre- and neoplastic lesions compared with normal and benign. The PLS-DA showed that the groups could be discriminated with 90.3% accuracy when the mean-subtracted spectra were used, contrasting with 75.1% accuracy when the non-subtracted spectra were used. Also, when discriminating non-neoplastic tissue (Normal + BEN) from pre- and neoplastic sites (BCC + SCC + KERAT), the accuracy increases to 92.5% for the normal-subtracted compared with 85.3% for the non-subtracted. The subtraction of the mean normal spectrum from the subject obtained circumjacent to each lesion could significantly increase the diagnostic capability of the Raman-based discrimination algorithm.

Discrimination of non-melanoma skin lesions from non-tumor human skin tissues in vivo using Raman spectroscopy and multivariate statistics.

BACKGROUND AND OBJECTIVE: Raman spectroscopy was used to discriminate human non-melanoma skin lesions from non-tumor tissues in vivo. This work proposed the discrimination between non-melanoma (basal cell carcinoma, BCC; squamous cell carcinoma, SCC) and pre-cancerous lesions (actinic keratosis, AK) from benign lesions and normal (non-tumor group, NT) tissues, using near-infrared Raman spectroscopy with a Raman probe. MATERIALS AND METHODS: Prior to surgery, the spectra of suspicious lesions were obtained in situ. The spectra of adjacent, clinically normal skin were also obtained. Lesions were resectioned and submitted for histopathology. The Raman spectra were measured using a Raman spectrometer (830 nm). Two types of discrimination models were developed to distinguish the different histopathological groups. The principal components analysis discriminant analysis (PCA/DA) and the partial least squares discriminant analysis (PLS/DA) were based on Euclidean, quadratic and Mahalanobis distances. RESULTS: PCA and PLS spectral vectors showed spectral features of skin constituents, such as lipids (between 1,250 cm(-1) and 1,300 cm(-1) and at 1,450 cm(-1)) and proteins (between 870 cm(-1) and 940 cm(-1), 1,240 cm(-1) and 1,271 cm(-1), and at 1,000 cm(-1) and 1,450 cm(-1)). Despite the small spectral differences between malignant lesions and benign tissues, the algorithms discriminated the spectra of non-melanoma skin and pre-cancerous lesions from benign and normal tissues, with an overall accuracy of 82.8% and 91.9%, respectively. CONCLUSION: PCA and PLS could discriminate Raman spectra of skin tissues, opening the way for an in vivo optical diagnosis.

Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals.

Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis.

OBJECTIVE: Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. BACKGROUND DATA: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. METHODS: Skin biopsy fragments of ≈ 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. RESULTS: We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p<0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. CONCLUSIONS: This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

Differentiating normal and basal cell carcinoma human skin tissues in vitro using dispersive Raman spectroscopy: a comparison between principal components analysis and simplified biochemical models.

OBJECTIVE: Raman spectroscopy has been used to detect spectral differences between normal and basocellular cell carcinoma (BCC) skin tissues that are related to biochemical alterations between tissues. BACKGROUND DATA: Raman spectroscopy is an analytic tool that could detect biochemical alterations in tissues, and its use would lead to real-time and less-invasive cancer diagnosis. METHODS: Raman spectra from human tissue fragments (normal and BCC) were obtained in a dispersive, near-infrared Raman spectrometer (laser parameters: 830 nm, 80 mW) with a CCD detector. Spectral changes between normal and BCC were analyzed with a principal components analysis (PCA) algorithm and a simplified biochemical model based on the relative amount of collagen and cell fat extracted from tissue Raman spectra. RESULTS: Main spectral differences between these samples were in the region of 800 to 1,000 per centimeter and 1,200 to 1,300 per centimeter, corresponding to vibrational bands from lipids and proteins (C-C bonds and amide III, respectively). The diagnostic algorithm based on PCA and Mahalanobis distance applied to the scores of principal components vectors PC1 and PC2 could identify tissue with sensitivity and specificity of 89% and 93%, respectively, for the training group and 96% and 92% for the prospective group. The simplified biochemical model for collagen amount had sensitivity and specificity of 95% and 83% for the training group and 87% and 92% for the prospective group. CONCLUSIONS: Raman spectroscopy could differentiate between normal and BCC tissues in both the PCA and biochemical models, showing higher sensitivity and specificity for the PCA model, although the simplified biochemical model is easier to implement.

Radioguided breast surgery for occult lesion localization - correlation between two methods.

BACKGROUND: The detection of sub-clinical breast lesions has increased with screening mammography. Biopsy techniques can offer precision and agility in its execution, as well as patient comfort. This trial compares radioguided occult lesion localization (ROLL) and wire-guided localization (WL) of breast lesions. We investigate if a procedure at the ambulatorial level (ROLL) could lead to a better aesthetic result and less postoperative pain. In addition, we intend to demonstrate the efficacy of radioguided localization and removal of occult breast lesions using radiopharmaceuticals injected directly into the lesions and correlate radiological and histopathological findings. METHODS: One hundred and twenty patients were randomized into two groups (59 WL and 61 ROLL). The patients were requested to score the cosmetic appearance of their breast after surgery, and a numerical rating scale was used to measure pain on the first postoperative day. Clearance margins were considered at > or = 10 mm for invasive cancer, > or = 5 mm for ductal carcinoma in situ, and > or = 1 mm for benign disease. Patients were subsequently treated according to the definitive histological result. When appropriate, different statistical tests were used in order to test the significance between the two groups, considering a P value < 0.05 as statistically significant. RESULTS: WL and ROLL located all the occult breast lesions successfully. In the ROLL group, the specimen volume was smaller and there were more cases with clear margins (P < 0.05). There were significant differences in mean time of hospital stay between WL and ROLL (21.42 vs. 2.56 hours), but not in operative time (39.4 vs. 29.9 minutes). There were significant differences in the subjective ease of the procedures as rated by the patients (cosmetic outcomes and postoperative pain). CONCLUSION: ROLL is an effective method for the excision of non-palpable breast lesions. It enables more careful planning of the cutaneous incision, leading to better aesthetic results, less postoperative symptoms, and smaller volumes of excised tissue.

Colecistectomia durante a gravidez: relato de seis casos / Cholescystectomy during pregnancy: six cases related

Os autores apresentam seis casos de colecistite aguda, decorrentes de litíase biliar durante a gravidez. Os sintomas mais freqüentes foram febre, cólica biliar, calafrios, intolerância à gordura e vômitos. Todas as pacientes foram submetidas à colecistectomia durante a gravidez. Em cinco casos realizou-se colangiografia transoperatória, dos quais três pacientes foram submetidas à coledocotomia por coledocolitíase. A cirurgia é indicada quando não houver resposta ao tratamento clínico, na persistência de dor e(ou) febre, na presença de sinal de Courvoisier, assim como de sinais de irritação peritoneal e persistência de icterícia obstrutiva. O momento mais adequado para se realizar a cirurgia é uma importante consideração para que se diminua a morbimortalidade fetal e materna.

Doença de Von Recklinghausen: relato de caso / Von Recklinghausen disease: a case report

O reconhecimento e a conduta das patologias requer rotina e embasamento científico dos profissionais da área de saúde. Os autores discutem a conduta frente aos nódulos de subcutâneo baseados no relato de caso de um paciente cujo diagnóstico näo foi realizado por falta de encaminhamento ao estudo anátomo-patológico. Discutem-se os aspectos clínicos, fisiopatológicos e possibilidades terapêuticas na neurofibromatose (Doença de Von Recklinghausen), revisando-se a literatura