Clinical Efficacy and Safety of a Thermomechanical Fractional Injury Device for Neck Rejuvenation.

BACKGROUND: Neck rejuvenation has consistently become a popular cosmetic procedure. While various treatment modalities have been used, a novel fractional thermomechanical skin rejuvenation system was recently developed to create dermal coagulation through direct heat transfer with subsequent neocollagenesis. OBJECTIVE: A prospective clinical trial evaluated the efficacy and safety of a thermomechanical fractional injury device (Tixel 2, Novoxel, Netanya, Israel) for neck rhytides. MATERIALS AND METHODS: Subjects with moderate to severe neck rhytides were enrolled for 4 monthly treatments. RESULTS: Twenty-six subjects were enrolled and completed all study visits. The mean age was 58.4 years, and 100.0% were women. Fitzpatrick skin types I to IV were included. For Fitzpatrick Wrinkle Classification System (FWCS), the mean baseline score was 6.3. As per investigator, there was a mean 1.5-grade improvement in FWCS at 1-month follow-up (p < .00001) and 1.4-grade improvement in FWCS at 3-month follow-up (p < .00001). For physician Global Aesthetic Improvement Scale, all subjects (100%) had improvement at both 1- and 3-month follow-up visits. There were no severe adverse events, and subjects experienced minimal pain. CONCLUSION: A novel thermomechanical fractional injury device is effective and safe for the treatment of neck rhytides.

Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis.

BACKGROUND: Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps. OBJECTIVE: The aim of this systematic review was to offer an in-depth exploration of published AI algorithms trained on dermoscopic and macroscopic clinical images for the diagnosis of melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma (cSCC). METHODS: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic review was conducted on peer-reviewed articles published between January 1, 2000, and January 26, 2023. RESULTS AND DISCUSSION: Among the 232 studies in this review, the overall accuracy, sensitivity, and specificity of AI for tumor detection averaged 90%, 87%, and 91%, respectively. Model performance improved with time. Despite seemingly impressive performance, the paucity of external validation and limited representation of cSCC and skin of color in the data sets limits the generalizability of the current models. In addition, dermatologists coauthored only 12.9% of all studies included in the review. Moving forward, it is imperative to prioritize robustness in data reporting, inclusivity in data collection, and interdisciplinary collaboration to ensure the development of equitable and effective AI tools.

High-Intensity, High-Frequency, Parallel Ultrasound Beams for Cellulite of the Buttocks and Thighs.

BACKGROUND: Ultrasound energy can successfully treat fine lines and wrinkles, as well as lift the eyebrow and submentum. Ultrasound waves of high intensity induce thermal injury in the dermis with subsequent tissue remodeling. OBJECTIVE: To examine the utility of a novel ultrasound device that utilizes high-intensity, high-frequency, parallel ultrasound beams to improve the clinical appearance of cellulite on the thighs and buttocks. MATERIALS AND METHODS: A prospective, multicenter, clinical study investigated this novel ultrasound device using 2 treatments. RESULTS: Sixty-five subjects completed both treatments. The mean age was 46 years, and 100% were women. Fitzpatrick skin types I to VI were represented. Assessments compared 3-month follow-up with baseline. Two blinded reviewers agreed in identifying pretreatment and post-treatment photographs for 89.2%. For Cellulite Severity Scale rating, there was significant improvement of 1.61 units (p < .001). For cellulite Global Aesthetic Improvement Scale (GAIS), 89.2% had improvement, with a mean of 0.87 units (p < .001). For Laxity Scale rating, there was significant improvement of 0.70 units (p < .001). For skin laxity GAIS, 89.2% had improvement, with a mean of 0.76 units (p < .001). No device-related adverse events occurred. CONCLUSION: A novel ultrasound device that utilizes high-intensity, high-frequency, parallel ultrasound beams can safely and effectively improve the clinical appearance of cellulite on the thighs and buttocks.

High-volume facilities are significantly more likely to use guideline-adherent systemic immunotherapy for metastatic Merkel cell carcinoma: implications for cancer care regionalization.

Merkel cell carcinoma (MCC) is a neuroendocrine skin cancer with a high rate of mortality. While still relatively rare, the incidence of MCC has been rapidly rising in the US and around the world. Since 2017, two immunotherapeutic drugs, avelumab and pembrolizumab, have been FDA-approved for the treatment of metastatic MCC and have revolutionized outcomes for MCC. However, real-world outcomes can differ from clinical trial data, and the adoption of novel therapeutics can be gradual. We aimed to characterize the treatment practices and outcomes of patients with metastatic MCC across the US. A retrospective cohort study of adult cases of MCC in the National Cancer Database diagnosed from 2004 to 2019 was performed. Multivariable logistic regressions to determine the association of a variety of patient, tumor, and system factors with likelihood of receipt of systemic therapies were performed. Univariate Kaplan-Meier and multivariable Cox survival regressions were performed. We identified 1017 cases of metastatic MCC. From 2017 to 2019, 54.2% of patients received immunotherapy. This increased from 45.1% in 2017 to 63.0% in 2019. High-volume centers were significantly more likely to use immunotherapy (odds ratio 3.235, p = 0.002). On univariate analysis, patients receiving systemic immunotherapy had significantly improved overall survival (p < 0.001). One-, 3-, and 5-year survival was 47.2% (standard error [SE] 1.8%), 21.8% (SE 1.5%), and 16.5% (SE 1.4%), respectively, for patients who did not receive immunotherapy versus 62.7% (SE 3.5%), 34.4% (SE 3.9%), and 23.6% (SE 4.4%), respectively, for those who did (Fig. 1). In our multivariable survival regression, receipt of immunotherapy was associated with an approximately 35% reduction in hazard of death (hazard ratio 0.665, p < 0.001; 95% CI 0.548-0.808). Our results demonstrate that the real-world survival advantage of immunotherapy for metastatic MCC is similar to clinical trial data. However, many patients with metastatic disease did not receive this guideline-recommended therapy in our most recent study year, and use of immunotherapy is higher at high-volume centers. This suggests that regionalization of care to high-volume centers or dissemination of their practices, may ultimately improve patient survival.

Metabolomic Profiles of Human Glioma Inform Patient Survival.

Aims: Targeting tumor metabolism may improve the outcomes for patients with glioblastoma (GBM). To further preclinical efforts targeting metabolism in GBM, we tested the hypothesis that brain tumors can be stratified into distinct metabolic groups with different patient outcomes. Therefore, to determine if tumor metabolites relate to patient survival, we profiled the metabolomes of human gliomas and correlated metabolic information with clinical data. Results: We found that isocitrate dehydrogenase-wildtype (IDHwt) GBMs are metabolically distinguishable from IDH mutated (IDHmut) astrocytomas and oligodendrogliomas. Survival of patients with IDHmut gliomas was expectedly more favorable than those with IDHwt GBM, and metabolic signatures can stratify IDHwt GBMs subtypes with varying prognoses. Patients whose GBMs were enriched in amino acids had improved survival, while those whose tumors were enriched for nucleotides, redox molecules, and lipid metabolites fared more poorly. These findings were recapitulated in validation cohorts using both metabolomic and transcriptomic data. Innovation: Our results suggest the existence of metabolic subtypes of GBM with differing prognoses, and further support the concept that metabolism may drive the aggressiveness of human gliomas. Conclusions: Our data show that metabolic signatures of human gliomas can inform patient survival. These findings may be used clinically to tailor novel metabolically targeted agents for GBM patients with different metabolic phenotypes. Antioxid. Redox Signal. 39, 942-956.

Predictors of large cell transformation in patients with Sezary Syndrome-A retrospective analysis.

BACKGROUND: Large cell transformation (LCT) of Sezary Syndrome (SS) is a rare phenomenon. To date, there are no rigorous studies identifying risk factors for its development. OBJECTIVES: Here, we seek to characterize the clinicopathologic risk factors that predispose patients with SS to develop LCT. METHODS: We retrospectively evaluated all SS patient records available in the Michigan Medicine Cancer Registry from 2010-2021. Clinical and pathologic variables were compared between groups. The Kaplan-Meier method and log-rank test were used to assess overall survival. RESULTS: Of 28 SS patients identified, eight patients experienced LCT, and 20 did not (NLCT). Peak lactate dehydrogenase (LDH) before LCT (p = 0.0012), maximum total body surface area (TBSA) involvement before LCT (p = 0.0114), absolute CD8+ cell count measured on flow cytometry at diagnosis of SS (p = 0.0455) and at the most recent blood draw (p = 0.00736), and ulceration on biopsy (p = 0.0034) were significant clinicopathologic variables identified between the SS patients that developed LCT versus those that did not. CONCLUSIONS: Maximum TBSA involvement, peak LDH, presence of ulceration, and decreased levels of CD8+ cells in the peripheral blood may predict the development of LCT in patients with SS.

Intermediate clinical endpoints for surrogacy in localised prostate cancer: an aggregate meta-analysis.

BACKGROUND: The international Intermediate Clinical Endpoints in Cancer of the Prostate working group has established metastasis-free survival as a surrogate for overall survival in localised prostate cancer based on the findings of 19 predominantly radiotherapy-based trials. We sought to comprehensively assess aggregate trial-level performance of commonly reported intermediate clinical endpoints across all randomised trials in localised prostate cancer. METHODS: For this meta-analysis, we searched PubMed for all trials in localised or biochemically recurrent prostate cancer published between Jan 1, 1970, and Jan 15, 2020. Eligible trials had to be randomised, therapeutic, reporting overall survival and at least one intermediate clinical endpoint, and with a sample size of at least 70 participants. Trials of metastatic disease were excluded. Intermediate clinical endpoints included biochemical failure, local failure, distant metastases, biochemical failure-free survival, progression-free survival, and metastasis-free survival. Candidacy for surrogacy was assessed using the second condition of the meta-analytical approach (ie, correlation of the treatment effect of the intermediate clinical endpoint and overall survival), using R2 weighted by the inverse variance of the log intermediate clinical endpoint hazard ratio. The intermediate clinical endpoint was deemed to be a surrogate for overall survival if R2 was 0·7 or greater. FINDINGS: 75 trials (53 631 patients) were included in our analysis. Median follow-up was 9·1 years (IQR 5·7-10·6). Biochemical failure (R2 0·38 [95% CI 0·11-0·64]), biochemical failure-free survival (R2 0·12 [0·0030-0·33]), biochemical failure and clinical failure (R2 0·28 [0·0045-0·65]), and local failure (R2 0·085 [0·00-0·37]) correlated poorly with overall survival. Progression-free survival (R2 0·46 [95% CI 0·22-0·67]) showed moderate correlation with overall survival, and metastasis-free survival (R2 0·78 [0·59-0·89]) correlated strongly. INTERPRETATION: Intermediate clinical endpoints based on biochemical and local failure did not meet the second condition of the meta-analytical approach and are not surrogate endpoints for overall survival in localised prostate cancer. Our findings validate metastasis-free survival as the only identified surrogate endpoint for overall survival to date. FUNDING: Prostate Cancer Foundation and National Institutes of Health.

Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks.

BACKGROUND: Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence. METHODS: We used fiber laser-based SRH, a label-free, nonconsumptive, high-resolution microscopy method (<60 sec per 1 × 1 mm2) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48). RESULTS: Using patch-level CNN predictions, the inference algorithm returns a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%. CONCLUSION: SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.

A Systematic Review of the Evidence for the Decipher Genomic Classifier in Prostate Cancer.

CONTEXT: Molecular biomarkers aim to address the established limitations of clinicopathologic factors to accurately risk stratify patients with prostate cancer (PCa). Questions remain as to whether sufficient evidence supports adoption of these biomarkers for clinical use. OBJECTIVE: To perform a systematic review of the available evidence supporting the clinical utility of the Decipher genomic classifier (GC). EVIDENCE ACQUISITION: The review was performed as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines by searching PubMed and conference abstracts from January 2010 to June 2020. Evidence was then graded using the criteria of Simon et al (Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst 2009;101:1446-52) and American Urology Association (AUA) criteria. EVIDENCE SYNTHESIS: In total, 42 studies and 30407 patients were included. GC performance data were available for localized, postprostatectomy, nonmetastatic castration-resistant, and metastatic hormone-sensitive PCa as part of retrospective studies (n=12141), prospective registries (n=17053), and prospective and post hoc randomized trial analyses (n=1213). In 32 studies (n=12600), the GC was independently prognostic for all study endpoints (adverse pathology, biochemical failure, metastasis, and cancer-specific and overall survival) on multivariable analysis and improved the discrimination over standard of care in 24 studies (n=8543). GC use changed the management in active surveillance (number needed to test [NNT]=9) and postprostatectomy (NNT=1.5-4) settings in five studies (n=4331). Evidence strength was levels 1 and 2 by the Simon criteria for all disease states other than high-risk PCa, and grades A and B by AUA criteria depending on disease state. CONCLUSIONS: Consistent data are now present from diverse levels of evidence, which when viewed together, have demonstrated clinical utility of the GC in PCa. The utility of the GC is strongest for intermediate-risk PCa and postprostatectomy decision-making. PATIENT SUMMARY: In this paper, we review the evidence of the Decipher genomic classification tool for men with prostate cancer. We found consistent evidence that the test helps identify which cancers are more or less aggressive, which in turn aids in personalized treatment decision-making.

Prognostic value of intratumoral lymphocyte-to-monocyte ratio and M0 macrophage enrichment in tumor immune microenvironment of melanoma.

Skin cutaneous melanoma is characterized by significant heterogeneity in its molecular, genomic and immunologic features. Whole transcriptome RNA sequencing data from The Cancer Genome Atlas of skin cutaneous melanoma (n = 328) was utilized. CIBERSORT was used to identify immune cell type composition, on which unsupervised hierarchical clustering was performed. Analysis of overall survival was performed using Kaplan-Meier estimates and multivariate Cox regression analyses. Membership in the lymphocyte:monocytelow, monocytehi gh and M0high cluster was an independently poor prognostic factor for survival (HR: 3.03; 95% CI: 1.12-8.20; p = 0.029) and correlated with decreased predicted response to immune checkpoint blockade. In conclusion, an M0-macrophage-enriched, lymphocyte-to-monocyte-ratio-low phenotype in the primary melanoma tumor site independently characterizes an aggressive phenotype that may differentially respond to treatment.

Characterization of the Tumor Immune Microenvironment Identifies M0 Macrophage-Enriched Cluster as a Poor Prognostic Factor in Hepatocellular Carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and a high recurrence rate. The tumor immune microenvironment in HCC has been characterized as shifted toward immunosuppression. We conducted a genomic data-driven classification of immune microenvironment HCC subtypes. In addition, we demonstrated their prognostic value and suggested a potential therapeutic targeting strategy. METHODS: RNA sequencing data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma was used (n = 366). Abundance of immune cells was imputed using CIBERSORT and visualized using unsupervised hierarchic clustering. Overall survival (OS) was analyzed using Kaplan-Meier estimates and Cox regression. Differential expression and gene set enrichment analyses were conducted on immune clusters with poor OS and high programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) coexpression. A scoring metric combining differentially expressed genes and immune cell content was created, and its prognostic value and immune checkpoint blockade response prediction was evaluated. RESULTS: Two clusters were characterized by macrophage enrichment, with distinct M0Hi and M2Hi subtypes. M2Hi (P = .038) and M0Hi (P = .018) were independently prognostic for OS on multivariable analysis. Kaplan-Meier estimates demonstrated that patients in M0Hi and M2Hi treated with sorafenib had decreased OS (P = .041), and angiogenesis hallmark genes were enriched in the M0Hi group. CXCL6 and POSTN were overexpressed in both the M0Hi and the PD-1Hi/PD-L1Hi groups. A score consisting of CXCL6 and POSTN expression and absolute M0 macrophage content was discriminatory for OS (intermediate: hazard ratio [HR], 1.59; P ≤ .001; unfavorable: HR, 2.08; P = .04). CONCLUSION: Distinct immune cell clusters with macrophage predominance characterize an aggressive HCC phenotype, defined molecularly by angiogenic gene enrichment and clinically by poor prognosis and sorafenib response. This novel immunogenomic signature may aid in stratification of unresectable patients to receive checkpoint inhibitor and antiangiogenic therapy combinations.

Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic.

Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.

Tumor Immune Microenvironment Clusters in Localized Prostate Adenocarcinoma: Prognostic Impact of Macrophage Enriched/Plasma Cell Non-Enriched Subtypes.

BACKGROUND: Prostate cancer (PCa) is characterized by significant heterogeneity in its molecular, genomic, and immunologic characteristics. METHODS: Whole transcriptome RNAseq data from The Cancer Genome Atlas of prostate adenocarcinomas (n = 492) was utilized. The immune microenvironment was characterized using the CIBERSORTX tool to identify immune cell type composition. Unsupervised hierarchical clustering was performed based on immune cell type content. Analyses of progression-free survival (PFS), distant metastases, and overall survival (OS) were performed using Kaplan-Meier estimates and Cox regression multivariable analyses. RESULTS: Four immune clusters were identified, largely defined by plasma cell, CD4+ Memory Resting T Cells (CD4 MR), and M0 and M2 macrophage content (CD4 MRHighPlasma CellHighM0LowM2Mid, CD4 MRLowPlasma CellHighM0LowM2Low, CD4 MRHighPlasma CellLowM0HighM2Low, and CD4 MRHighPlasma CellLowM0LowM2High). The two macrophage-enriched/plasma cell non-enriched clusters (3 and 4) demonstrated worse PFS (HR 2.24, 95% CI 1.46-3.45, p = 0.0002) than the clusters 1 and 2. No metastatic events occurred in the plasma cell enriched, non-macrophage-enriched clusters. Comparing clusters 3 vs. 4, in patients treated by surgery alone, cluster 3 had zero progression events (p < 0.0001). However, cluster 3 patients had worse outcomes after post-operative radiotherapy (p = 0.018). CONCLUSION: Distinct tumor immune clusters with a macrophage-enriched, plasma cell non-enriched phenotype and reduced plasma cell enrichment independently characterize an aggressive phenotype in localized prostate cancer that may differentially respond to treatment.

Automated histologic diagnosis of CNS tumors with machine learning.

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.