Optimization of multimodal spectral imaging for assessment of resection margins during Mohs micrographic surgery for basal cell carcinoma.

Multimodal spectral imaging (MSI) based on auto-fluorescence imaging and Raman micro-spectroscopy was used to detect basal cell carcinoma (BCC) in tissue specimens excised during Mohs micrographic surgery. In this study, the MSI algorithm was optimized to maximize the diagnosis accuracy while minimizing the number of Raman spectra: the segmentation of the auto-fluorescence images was optimized according to the type of BCC, sampling points for Raman spectroscopy were generated based on auto-fluorescence intensity variance and segment area, additional Raman spectra were acquired when performance of the segmentation algorithm was sub-optimal. The results indicate that accurate diagnosis can be achieved with a sampling density of ~2,000 Raman spectra/cm(2), based on sampling points generated by the MSI algorithms. The key benefit of MSI is that diagnosis of BCC is obtained based on intrinsic chemical contrast of the tissue, within time scales similar to frozen-section histopathology, but without requiring laborious sample preparation and subjective interpretation of stained frozen-sections.

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy.

Tissue-conserving surgery is used increasingly in cancer treatment. However, one of the main challenges in this type of surgery is the detection of tumor margins. Histopathology based on tissue sectioning and staining has been the gold standard for cancer diagnosis for more than a century. However, its use during tissue-conserving surgery is limited by time-consuming tissue preparation steps (1-2 h) and the diagnostic variability inherent in subjective image interpretation. Here, we demonstrate an integrated optical technique based on tissue autofluorescence imaging (high sensitivity and high speed but low specificity) and Raman scattering (high sensitivity and high specificity but low speed) that can overcome these limitations. Automated segmentation of autofluorescence images was used to select and prioritize the sampling points for Raman spectroscopy, which then was used to establish the diagnosis based on a spectral classification model (100% sensitivity, 92% specificity per spectrum). This automated sampling strategy allowed objective diagnosis of basal cell carcinoma in skin tissue samples excised during Mohs micrographic surgery faster than frozen section histopathology, and one or two orders of magnitude faster than previous techniques based on infrared or Raman microscopy. We also show that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. This study demonstrates the potential of this technique to provide a rapid and objective intraoperative method to spare healthy tissue and reduce unnecessary surgery by determining whether tumor cells have been removed.

Rapid acquisition of Raman spectral maps through minimal sampling: applications in tissue imaging.

A method is presented for acquiring high-spatial-resolution spectral maps, in particular for Raman micro-spectroscopy (RMS), by selectively sampling the spatial features of interest and interpolating the results. This method achieves up to 30 times reduction in the sampling time compared to raster-scanning, the resulting images have excellent correlation with conventional histopathological staining, and are achieved with sufficient spectral signal-to-noise ratio to identify individual tissue structures. The benefits of this selective sampling method are not limited to tissue imaging however; it is expected that the method may be applied to other techniques which employ point-by-point mapping of large substrates.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma.

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a "generalization" of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Dermatitis artefacta in a 12-year-old girl mimicking cutaneous T-cell lymphoma.

Dermatitis artefacta or factitious disease may be unrecognized in children. We present a 12-year-old girl who had an unusual facial lesion on the chin, which was self-inflicted but histologically mimicked cutaneous T-cell lymphoma. Our report emphasizes both the potential diagnostic pitfalls and the importance of clinicopathologic correlation.

Chilblain-like leukemia cutis.

We present a 6-year-old girl with an 8-month history of fluctuating chilblain-like lesions on the toes. A full blood count showed slight thrombocytopenia and monocytosis. A skin biopsy specimen showed a dense perivascular and superficial dermal cellular infiltrate which was CD3(-), CD43(+), and lysosyme + on immunocytochemistry, suggesting a monocytic origin. Juvenile myelomonocytic leukemia was diagnosed after a bone marrow biopsy and spontaneous marrow colony growth in culture.