Evaluation of non-invasive multispectral imaging as a tool for measuring the effect of systemic therapy in Kaposi sarcoma

Current and Future Tools for Diagnosis of Kaposi's Sarcoma

Simple Summary

Kaposi’s sarcoma, a rare opportunistic tumor, is observed in four epidemiological conditions (AIDS-related, iatrogenic, endemic or classic KS). Although in most cases KS is an indolent disease, it can be locally aggressive and/or it can invade other organs than the skin, resulting in more severe presentations, especially in patients with severe immunosuppression. There is no consensus on the imaging workup that is necessary for either the initial staging of the disease or the follow-up. Future perspectives include the use of certain non-invasive imaging tools that may help to evaluate the clinical response to treatment, as well as certain new histological markers that may help in guiding the treatment planning for this atypical neoplasm.

Abstract

Kaposi’s sarcoma (KS) is a rare, atypical malignancy associated with immunosuppression and can be qualified as an opportunistic tumor, which responds to immune modulation or restoration. Four different epidemiological forms have been individualized (AIDS-related, iatrogenic, endemic or classic KS). Although clinical examination is sufficient to diagnose cutaneous lesions of KS, additional explorations are necessary in order to detect lesions involving other organs. New histological markers have been developed in recent years concerning the detection of HHV-8 latent or lytic proteins in the lesions, helping to confirm the diagnosis when it is clinically doubtful. More recently, the evaluation of the local immune response has also been shown to provide some guidance in choosing the appropriate therapeutic option when necessary. We also review the indication and the results of conventional radiological imaging and of non-invasive imaging tools such as ¹⁸F-fluoro-deoxy-glucose positron emission tomography, thermography and laser Doppler imaging for the diagnosis of KS and for the follow-up of therapeutic response in patients requiring systemic treatment.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

18F-fluorodeoxyglucose Positron Emission Tomography in Kaposi Sarcoma Herpesvirus-Associated Multicentric Castleman Disease: Correlation With Activity, Severity, Inflammatory and Virologic Parameters

BACKGROUND

Kaposi sarcoma herpesvirus (KSHV)-associated multicentric Castleman disease (MCD) is a lymphoproliferative inflammatory disorder commonly associated with human immunodeficiency virus (HIV). Its presentation may be difficult to distinguish from HIV and its complications, including lymphoma. Novel imaging strategies could address these problems.

METHODS

We prospectively characterized (18)F-fluorodeoxyglucose positron emission tomography (PET) findings in 27 patients with KSHV-MCD. Patients were imaged with disease activity and at remission with scans evaluated blind to clinical status. Symptoms, C-reactive protein level, and HIV and KSHV loads were assessed in relation to imaging findings.

RESULTS

KSHV-MCD activity was associated with hypermetabolic symmetric lymphadenopathy (median maximal standardized uptake value [SUVmax], 6.0; range, 2.0-8.0) and splenomegaly (3.4; 1.2-11.0), with increased metabolism also noted in the marrow (2.1; range, 1.0-3.5) and salivary glands (3.0; range, 2.0-6.0). The (18)F-fluorodeoxyglucose PET abnormalities improved at remission, with significant SUVmax decreases in the lymph nodes (P = .004), spleen (P = .008), marrow (P = .004), and salivary glands (P = .004). Nodal SUVmax correlated with symptom severity (P = .005), C-reactive protein level (R = 0.62; P = .004), and KSHV load (R = 0.54; P = .02) but not HIV load (P = .52).

CONCLUSIONS

KSHV-MCD activity is associated with (18)F-FDG PET abnormalities of the lymph nodes, spleen, marrow, and salivary glands. These findings have clinical implications for the diagnosis and monitoring of KSHV-MCD and shed light on its pathobiologic mechanism.

The prognostic significance of serum and cerebrospinal fluid MMP-9, CCL2 and sVCAM-1 in leukemia CNS metastasis

Metastasis to the central nervous system (CNS) is the primary obstacle in leukemia treatment. Matrix metalloproteinase-9 (MMP-9), chemokine ligand-2 (CCL2) and soluble vascular adhesion molecule-1 (sVCAM-1) play crucial roles in tumor cell adhesion, motivation and survival, but their roles in leukemia CNS metastasis remain to be elucidated. We investigated the prognostic significance of serum and cerebrospinal fluid (CSF) MMP-9, CCL2 and sVCAM-1 in leukemia patients to explore their potential as predictive biomarkers of the development of CNS leukemia (CNSL). MMP-9, CCL2 and sVCAM-1 were measured in paired CSF and serum samples collecting from 33 leukemia patients with or without CNS metastasis. Other risk factors related to CNSL prognosis were also analyzed. sVCAM-1Serum and CCL2Serum/CSF were significantly higher in the CNSL group than in the non-CNSL group and the controls (p < 0.05). MMP-9Serum was insignificantly lower in the CNSL group than in the non-CNSL group and the controls (p > 0.05). No differences were found for the sVCAM-1Serum, CCL2Serum, and MMP-9Serum levels between non-CNSL patients and controls (p > 0.05). MMP-9CSF was significantly higher in the CNSL group than both the non-CNSL and the control groups (p < 0.05). The indexes of sVCAM-1, CCL2, and MMP-9 in the CNSL group were lower than in the controls (p < 0.05). Positive correlations were determined between the MMP-9CSF and the ALBCSF/BBB value/WBCCSF, between sVCAM-1Serum and the WBCCSF/BBB value. Negative correlations existed between MMP-9Serum and the ALBCSF/BBB value/WBCCSF, and between the CCL2 index and ALBCSF. sVCAM-1Serum was positively associated with event-free survival (EFS), and patients with higher levels of ALBCSF, MMP-9CSF/Serum, CCL2CSF/Serum, and sVCAM-1CSF/Serum had shorter EFS. MMP-9CSF, CCL2CSF and sVCAM-1CSF are the first three principal components analyzed by cluster and principal component analysis. Our data suggest that MMP-9, CCL2 and sVCAM-1 in the CSF may be more potent than serum in predicting the possibility of leukemia metastatic CNS and the outcome of CNSL patients.

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.