The Role and Potential of 18F-FDG PET/CT in Malignant Melanoma: Prognostication, Monitoring Response to Targeted and Immunotherapy, and Radiomics

Targeting anticancer immunity in melanoma tumour microenvironment: unleashing the potential of adjuvants, drugs, and phytochemicals

Melanoma poses a challenge in oncology because of its aggressive nature and limited treatment modalities. The tumour microenvironment (TME) in melanoma contains unique properties such as an immunosuppressive and high-density environment, unusual vasculature, and a high number of stromal and immunosuppressive cells. In recent years, numerous experiments have focused on boosting the immune system to effectively remove malignant cells. Adjuvants, consisting of phytochemicals, toll-like receptor (TLR) agonists, and cytokines, have shown encouraging results in triggering antitumor immunity and augmenting the therapeutic effectiveness of anticancer therapy. These adjuvants can stimulate the maturation of dendritic cells (DCs) and infiltration of cytotoxic CD8+ T lymphocytes (CTLs). Furthermore, nanocarriers can help to deliver immunomodulators and antigens directly to the tumour stroma, thereby improving their efficacy against malignant cells. The remodelling of melanoma TME utilising phytochemicals, agonists, and other adjuvants can be combined with current modalities for improving therapy outcomes. This review article explores the potential of adjuvants, drugs, and their nanoformulations in enhancing the anticancer potency of macrophages, CTLs, and natural killer (NK) cells. Additionally, the capacity of these agents to repress the function of immunosuppressive components of melanoma TME, such as immunosuppressive subsets of macrophages, stromal and myeloid cells will be discussed.

Neurological autoimmunity in melanoma patients: a comparison between those exposed and non-exposed to immune checkpoint inhibitors

BACKGROUND

The clinical spectrum of melanoma-associated neurological autoimmunity, whether melanoma-associated paraneoplastic neurological syndromes (PNS) or induced by immune checkpoint inhibitors (ICI), is not well characterized. We aim to describe the clinical spectrum of melanoma-associated neurological autoimmunity.

METHODS

A systematic review of the literature combined with patients from French databases of paraneoplastic neurological syndromes was conducted. All melanoma patients with a possible immune-mediated neurologic syndrome were included and classified according to whether they had previously been exposed to ICI (ICI-neurotoxicity) or not (ICI-naïve) at first neurological symptoms.

RESULTS

Seventy ICI-naïve (literature: n = 61) and 241 ICI-neurotoxicity patients (literature: n = 180) were identified. Neuromuscular manifestations predominated in both groups, but peripheral neuropathies were more frequent in ICI-neurotoxicity patients (39.4% vs 21.4%, p = 0.005) whereas myositis was more frequent in ICI-naïve patients (42.9% vs 18.7%, p < 0.001). ICI-naïve patients had also more frequent central nervous system (CNS) involvement (35.7% vs 23.7%, p = 0.045), classical paraneoplastic syndrome (25.7% vs 5.8%, p < 0.001), and more frequently positive for anti-neuron antibodies (24/32, 75.0% vs 38/90, 42.2%, p = 0.001). Although more ICI-neurotoxicity patients died during the acute phase (22/202, 10.9% vs 1/51, 2.0%, p = 0.047), mostly myositis patients (14/22, 63.6%), mortality during follow-up was higher in ICI-naïve patients (58.5% vs 29.8%, p < 0.001). There was no significant difference in the frequency of life independence (mRS ≤ 2) in the surviving patients in both groups (95.5% vs 91.0%, p = 0.437).

CONCLUSIONS

Melanoma-associated PNS appear remarkably rare. The clinical similarities observed in neurological autoimmunity between ICI-treated and ICI-naïve patients, characterized predominantly by demyelinating polyradiculoneuropathy and myositis, suggest a potential prior immunization against melanoma antigens contributing to ICI-related neurotoxicity.

Positron Emission Tomography/Computed Tomography Transformation of Oncology: Melanoma and Skin Malignancies

Skin cancers are the most common cancers, with melanoma resulting in the highest cause of death in this category. Accurate clinical, histologic, and imaging staging with fludeoxyglucose positron emission tomography (FDG PET) is most important to guide patient management. Whilst surgical excision with clear margins is the gold-standard treatment for primary cutaneous melanoma, targeted therapies have generated remarkable and rapid clinical responses in melanoma, for which FDG PET also plays an important role in assessment of treatment response and post-therapy surveillance. Non-FDG PET tracers, advanced PET technology, and PET radiomics may potentially change the landscape of the utilization of PET in the imaging of patients with cutaneous malignancies.

The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective

Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. A keyword search of the Medline database was performed to assess the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma. Full-text articles were reviewed if they related to dermatoscopy, pathological slide assessment or radiology. Through analysis of 95 studies, we demonstrate that diagnostic accuracy of AI/CNN can be superior (or at least equal) to clinicians. However, variability in image acquisition, pre-processing, segmentation, and feature extraction remains challenging. With current technological abilities, AI/CNN and clinicians synergistically working together are better than one another in all subspecialty domains relating to cutaneous melanoma. AI has the potential to enhance the diagnostic capabilities of junior dermatology trainees, primary care skin cancer clinicians and general practitioners. For experienced clinicians, AI provides a cost-efficient second opinion. From a pathological and radiological perspective, CNN has the potential to improve workflow efficiency, allowing clinicians to achieve more in a finite amount of time. Until the challenges of AI/CNN are reliably met, however, they can only remain an adjunct to clinical decision-making.

Metabolic Profiling to Assess Response to Targeted and Immune Therapy in Melanoma

There is currently no consensus to determine which advanced melanoma patients will benefit from targeted therapy, immunotherapy, or a combination of both, highlighting the critical need to identify early-response biomarkers to advanced melanoma therapy. The goal of this review is to provide scientific rationale to highlight the potential role of metabolic imaging to assess response to targeted and/or immune therapy in melanoma cancer. For that purpose, a brief overview of current melanoma treatments is provided. Then, current knowledge with respect to melanoma metabolism is described with an emphasis on major crosstalks between melanoma cell metabolism and signaling pathways involved in BRAF-targeted therapy as well as in immune checkpoint inhibition therapies. Finally, preclinical and clinical studies using metabolic imaging and/or profiling to assess response to melanoma treatment are summarized with a particular focus on PET (Positron Emission Tomography) imaging and 13C-MRS (Magnetic Resonance Spectroscopy) methods.

Prognostic Value of Baseline 18F-FDG PET/CT to Predict Brain Metastasis Development in Melanoma Patients

To investigate the value of 18F-FDG-PET/CT in predicting the occurrence of brain metastases in melanoma patients, in this retrospective study 201 consecutive patients with pathology-proven melanoma, between 2008 and 2021, were reviewed. Those who underwent 18F-FDG-PET/CT for initial staging were considered eligible. Baseline assessment included histopathology, 18F-FDG-PET/CT, and brain MRI. Also, all patients had serial follow-ups for diagnosing brain metastasis development. Baseline 18F-FDG-PET/CT parameters were analysed using competing risk regression models to analyze their correlation with the occurrence of brain metastases. Overall, 159 patients entered the study. The median follow-up was six years. Among clinical variables, the initial M-stage and TNM-stage were significantly correlated with brain metastasis. Regarding 18F-FDG-PET/CT parameters, regional metastatic lymph node uptake values, as well as prominent SULmax (pSULmax) and prominent SUVmean (pSUVmean), were significantly correlated with the outcome. Cumulative incidences were 10% (6.3-16%), 31% (24.4-38.9%), and 35.2% (28.5-43.5%) after 1, 5, and 10 years. There were significant correlations between pSULmax (p-value < 0.001) and pSULpeak (p-value < 0.001) and the occurrence of brain metastases. The higher these values, the sooner the patient developed brain metastases. Thus, baseline 18F-FDG-PET/CT may have the potential to predict brain metastasis in melanoma patients. Those with high total metabolic activity should undergo follow-up/complementary evaluations, such as brain MRI.

Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

BACKGROUND

Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT).

RESULTS

Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical-radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracies of the radiomics and clinical-radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer-Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model exhibited higher overall net benefit than the clinical model.

CONCLUSIONS

The PET/CT-based radiomics and clinical-radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.

Internal Thoracic Vein Tumor Thrombus From Sternal Melanoma Metastasis on 18 F-FDG PET/CT

ABSTRACT

We present 18 F-FDG PET/CT findings of an internal thoracic vein tumor thrombus from melanoma in a 76-year-old woman. Restaging 18 F-FDG PET/CT shows a progressive disease with an internal thoracic vein tumor thrombus from a sternal bone metastasis. Although cutaneous malignant melanoma may metastasize to any body part, a direct tumor invasion of veins and the formation of a tumor thrombus represent an extremely rare complication.

The role of imaging and sentinel lymph node biopsy in patients with T3b-T4b melanoma with clinically negative disease

Background

Previous studies demonstrate minimal utility of pre-operative imaging for low-risk melanoma; however, imaging may be more critical for patients with high-risk disease. Our study evaluates the impact of peri-operative cross-sectional imaging in patients with T3b-T4b melanoma.

Methods

Patients with T3b-T4b melanoma who underwent wide local excision were identified from a single institution (1/1/2005 - 12/31/2020). Cross-sectional imaging was defined as body CT, PET and/or MRI in the perioperative period, with the following findings: in-transit or nodal disease, metastatic disease, incidental cancer, or other. Propensity scores were created for the odds of undergoing pre-operative imaging. Recurrence free survival was analyzed using the Kaplan-Meier method and log-rank test.

Results

A total of 209 patients were identified with a median age of 65 (IQR 54-76), of which the majority were male (65.1%), with nodular melanoma (39.7%) and T4b disease (47.9%). Overall, 55.0% underwent pre-operative imaging. There were no differences in imaging findings between the pre- and post-operative cohorts. After propensity-score matching, there was no difference in recurrence free survival. Sentinel node biopsy was performed in 77.5% patients, with 47.5% resulting in a positive result.

Conclusion

Pre-operative cross-sectional imaging does not impact the management of patients with high-risk melanoma. Careful consideration of imaging use is critical in the management of these patients and highlights the importance of sentinel node biopsy for stratification and decision making.

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

Early diagnosis is essential for completely eradicating skin cancer and maximizing patients' clinical benefits. Emerging optical imaging modalities such as reflectance confocal microscopy (RCM), optical coherence tomography (OCT), magnetic resonance imaging (MRI), near-infrared (NIR) bioimaging, positron emission tomography (PET), and their combinations provide non-invasive imaging data that may help in the early detection of cutaneous tumors and surgical planning. Hence, they seem appropriate for observing dynamic processes such as blood flow, immune cell activation, and tumor energy metabolism, which may be relevant for disease evolution. This review discusses the latest technological and methodological advances in imaging techniques that may be applied for skin cancer detection and monitoring. In the first instance, we will describe the principle and prospective clinical applications of the most commonly used imaging techniques, highlighting the challenges and opportunities of their implementation in the clinical setting. We will also highlight how imaging techniques may complement the molecular and histological approaches in sharpening the non-invasive skin characterization, laying the ground for more personalized approaches in skin cancer patients.

Development of a Hybrid-Imaging-Based Prognostic Index for Metastasized-Melanoma Patients in Whole-Body 18F-FDG PET/CT and PET/MRI Data

Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.