Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

iPSC-cardiomyocytes in the preclinical prediction of candidate pharmaceutical toxicity.

Many challenges remain in the preclinical evaluation, adjudication, and prioritization of novel compounds in therapeutic discovery pipelines. These obstacles are evident by the large number of candidate or lead compounds failing to reach clinical trials, significantly due to a lack of efficacy in the disease paradigm of interest and/or the presence of innate chemical toxicity. The consequential compound attrition in discovery pipelines results in added monetary and time costs, potential danger to patients, and a slowed discovery of true therapeutics. The low rate of successful translation calls for improved models that can recapitulate in vivo function in preclinical testing to ensure the removal of toxic compounds earlier in the discovery process, in particular for the assessment of cardiotoxicity, the leading cause of post-market drug withdrawal. With recent advances in the development of human Inducible pluripotent stem cell derived cardiomyocytes (iPSC-CMs), novel compounds can be assessed with better disease relevance while more accurately assessing human safety. In this review, we discuss the utility of iPSC-CMs in preclinical testing by taking advantage of the inherent ability to mimic CMs in vivo. We explore the similarities and differences in electrophysiology, calcium handling, cellular signaling, contractile machinery, and metabolism between iPSC-CMs and adult CMs as these complex coordinated functions directly relate to toxicity evaluation. We will highlight considerations when using iPSC-CMs, such as maturation protocols, to ensure a more representative phenotype of the adult human CM, and how different populations of CMs can affect results in compound testing.

Incidence and profile of skin cancers in patients following ultraviolet phototherapy without psoralens: A retrospective cohort study.

BACKGROUND: Psoralen + ultraviolet-A (PUVA) is associated with photocarcinogenesis. However, carcinogenic risk with other ultraviolet phototherapies remains unclear. OBJECTIVE: Evaluate whether phototherapy without psoralens increases skin cancer risk. METHODS: Retrospective cohort study of patients treated at a teaching-hospital phototherapy center (1977-2018). Skin cancer records were validated against pathology reports. Age-standardized incidence rates (ASIRs) of skin cancer were evaluated for gender, skin phototype, diagnosis, ultraviolet modality, anatomical site; and compared to provincial population incidence rates (2003). RESULTS: In total, 3506 patients treated with broadband-ultraviolet-B, narrowband-UVB and/or combined UVAB were assessed with a mean follow-up of 7.3 years. Majority of patients had psoriasis (60.9%) or eczema (26.4%). Median number of treatments was 43 (1-3598). Overall, 170 skin cancers (17 melanoma, 33 squamous cell carcinoma and 120 basal cell carcinoma) occurred in 79 patients. Patient-based and tumor-based ASIR of skin cancer was 149 (95% CI: 112-187)/100,000 and 264 (219-309)/100,000 person-years, respectively. There was no significant difference between tumor-based ASIRs for melanoma, squamous cell carcinoma, and basal cell carcinoma compared to the general population; or in phototherapy patients with-psoriasis or eczema; or immunosuppressants. No cumulative dose-response correlation between UVB and skin cancer was seen. LIMITATIONS: Treatment and follow-up duration. CONCLUSION: No increased risk of melanoma and keratinocyte cancer was found with phototherapy.

Serum HCoV-spike specific antibodies do not protect against subsequent SARS-CoV-2 infection in children and adolescents.

SARS-CoV-2 infections in children are generally asymptomatic or mild and rarely progress to severe disease and hospitalization. Why this is so remains unclear. Here we explore the potential for protection due to pre-existing cross-reactive seasonal coronavirus antibodies and compare the rate of antibody decline for nucleocapsid and spike protein in serum and oral fluid against SARS-CoV-2 within the pediatric population. No differences in seasonal coronaviruses antibody concentrations were found at baseline between cases and controls, suggesting no protective effect from pre-existing immunity against seasonal coronaviruses. Antibodies against seasonal betacoronaviruses were boosted in response to SARS-CoV-2 infection. In serum, anti-nucleocapsid antibodies fell below the threshold of positivity more quickly than anti-spike protein antibodies. These findings add to our understanding of protection against infection with SARS-CoV-2 within the pediatric population, which is important when considering pediatric SARS-CoV-2 immunization policies.

Ethical, legal and social aspects of human cerebral organoids and their governance in Germany, the United Kingdom and the United States.

Human cerebral organoids (HCOs) are model systems that enable researchers to investigate the human brain in ways that had previously been impossible. The emergence of HCOs was accompanied by both expert and layperson discussions concerning the possibility of these novel entities developing sentience or consciousness. Such concerns are reflected in deliberations about how to handle and regulate their use. This perspective article resulted from an international and interdisciplinary research retreat "Ethical, Legal and Social Aspects of Human Cerebral Organoids and their Governance in Germany, the United Kingdom and the United States", which took place in Tübingen, Germany, in August 2022. The retreat focused on whether HCO research requires new ethical and regulatory approaches. It addressed epistemic issues around the detection and theorisation of consciousness, ethical concerns around moral status and research conduct, difficulties for legislation and guidelines managing these entities, and public engagement.

Full Stokes polarimetry using a monolithic off-axis polarizing interferometer and a 2D array sensor.

This paper describes a full Stokes polarimeter employing a monolithic off-axis polarizing interferometric module and a 2D array sensor. The proposed passive polarimeter provides a dynamic full Stokes vector measurement capability of around 30 Hz. As the proposed polarimeter employs no active devices and is operated by employing an imaging sensor, it has significant potential to become a highly compact polarization sensor for smartphone applications. To show the feasibility of the proposed passive dynamic polarimeter scheme, the full Stokes parameters of a quarter-wave plate are extracted and displayed on a Poincare sphere by varying the polarization state of the measured beam.

Micro-relief characterization of benign and malignant skin lesions by polarization speckle analysis in vivo.

BACKGROUND/PURPOSE: A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. METHODS: The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). RESULTS: The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 µm and 21 ± 3 µm, respectively. Normal skin has rms roughness of 31 ± 3 µm, other lesions have roughness of 35 ± 10 µm (AK), 35 ± 7 µm (SCC), 31 ± 4 µm (SK), and 30 ± 5 µm (BCC). CONCLUSION: An independent-samples Kruskal-Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection.

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma.

INTRODUCTION: We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS: Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS: Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION: Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

Using digital phenotyping to understand health-related outcomes: A scoping review.

BACKGROUND: Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations. OBJECTIVES: The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes. METHODS: A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. RESULTS: A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes. CONCLUSION: This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.

EFFECT OF ELEXACAFTOR/TEZACAFTOR/IVACAFTOR ON ANNUAL RATE OF LUNG FUNCTION DECLINE IN PEOPLE WITH CYSTIC FIBROSIS.

Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) was shown to be safe and efficacious in people with cystic fibrosis (CF) with ≥ 1 F508del-CFTR allele in Phase 3 clinical trials. ELX/TEZ/IVA treatment led to improved lung function, with increases in percent predicted forced expiratory volume in 1 second (ppFEV1) and Cystic Fibrosis Questionnaire-Revised respiratory domain score. Here, we evaluated the impact of ELX/TEZ/IVA on the rate of lung function decline over time by comparing changes in ppFEV1 in participants from the Phase 3 trials with a matched group of people with CF from the US Cystic Fibrosis Foundation Patient Registry not eligible for cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy. Participants treated with ELX/TEZ/IVA had on average no loss of pulmonary function over a 2-year period (mean annualized rate of change in ppFEV1, +0.39 percentage points [95% CI, -0.06 to 0.85]) compared with a 1.92 percentage point annual decline (95% CI, -2.16 to -1.69) in ppFEV1 in untreated controls. ELX/TEZ/IVA is the first CFTR modulator therapy shown to halt lung function decline over an extended time period.

SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images.

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin disease classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervised trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled MobileNetV2 can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performance. To the best of our knowledge, this is the first deep knowledge distillation application for multi-disease classification on the large-scale dermoscopy database. Our codes and models are available at https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis.

Landscape of mutations in early stage primary cutaneous melanoma: An InterMEL study.

It is unclear why some melanomas aggressively metastasize while others remain indolent. Available studies employing multi-omic profiling of melanomas are based on large primary or metastatic tumors. We examine the genomic landscape of early-stage melanomas diagnosed prior to the modern era of immunological treatments. Untreated cases with Stage II/III cutaneous melanoma were identified from institutions throughout the United States, Australia and Spain. FFPE tumor sections were profiled for mutation, methylation and microRNAs. Preliminary results from mutation profiling and clinical pathologic correlates show the distribution of four driver mutation sub-types: 31% BRAF; 18% NRAS; 21% NF1; 26% Triple Wild Type. BRAF mutant tumors had younger age at diagnosis, more associated nevi, more tumor infiltrating lymphocytes, and fewer thick tumors although at generally more advanced stage. NF1 mutant tumors were frequent on the head/neck in older patients with severe solar elastosis, thicker tumors but in earlier stages. Triple Wild Type tumors were predominantly male, frequently on the leg, with more perineural invasion. Mutations in TERT, TP53, CDKN2A and ARID2 were observed often, with TP53 mutations occurring particularly frequently in the NF1 sub-type. The InterMEL study will provide the most extensive multi-omic profiling of early-stage melanoma to date. Initial results demonstrate a nuanced understanding of the mutational and clinicopathological landscape of these early-stage tumors.

Polarization memory rate as a metric to differentiate benign and malignant tissues.

Non-invasive optical methods for cancer diagnostics, such as microscopy, spectroscopy, and polarimetry, are rapidly advancing. In this respect, finding new and powerful optical metrics is an indispensable task. Here we introduce polarization memory rate (PMR) as a sensitive metric for optical cancer diagnostics. PMR characterizes the preservation of circularly polarized light relative to linearly polarized light as light propagates in a medium. We hypothesize that because of well-known indicators associated with the morphological changes of cancer cells, like an enlarged nucleus size and higher chromatin density, PMR should be greater for cancerous than for the non-cancerous tissues. A thorough literature review reveals how this difference arises from the anomalous depolarization behaviour of many biological tissues. In physical terms, though most biological tissue primarily exhibits Mie scattering, it typically exhibits Rayleigh depolarization. However, in cancerous tissue the Mie depolarization regime becomes more prominent than Rayleigh. Experimental evidence of this metric is found in a preliminary clinical study using a novel Stokes polarimetry probe. We conducted in vivo measurements of 20 benign, 28 malignant and 59 normal skin sites with a 660 nm laser diode. The median PMR values for cancer vs non-cancer are significantly higher for cancer which supports our hypothesis. The reported fundamental differences in depolarization may persist for other types of cancer and create a conceptual basis for further developments in polarimetry applications for cancer detection.

Delivery planning for congenital lung malformations: A CVR based perinatal care algorithm.

PURPOSE: Congenital lung malformation (CLM) volume ratio (CVR) of ≥1.1 has been shown to be highly predictive of the need for urgent, perinatal surgical intervention. The purpose of this study was to utilize this information to propose a delivery planning and clinical management algorithm based on this threshold. METHODS: A retrospective cohort study was performed for all fetuses evaluated at our fetal center between 5/2015 and 11/2020. Demographics, ultrasound findings, late gestation CVR (≥27 weeks gestational age), prenatal and postnatal treatment, and outcomes were analyzed with nonparametric univariate analysis based on late gestation CVR of 1.1. Receiver operating characteristic curve analysis was performed to evaluate association between late gestation CVR, hydrops, need for fetal intervention, and need for urgent perinatal surgery. RESULTS: Of the 90 CLMs referred to our fetal center, 65 had late gestation CVR with a majority <1.1 (47/65, 72%). All patients with late gestation CVR ≥ 1.1 were managed with resection (18/18) with most resections requiring fetal intervention or urgent neonatal resection (13/18). Late gestation CVR < 1.1 were managed with elective resection (36/47, 77%) or non-operative observation (11/47, 23%). Late gestation CVR ≥ 1.1 had 100% sensitivity and NPV for hydrops, need for fetal intervention, and need for urgent perinatal surgery. CONCLUSION: CLM with CVR ≥ 1.1 were associated with urgent perinatal surgical intervention and expectant mothers should plan for delivery at centers equipped to manage neonatal resuscitation and potential urgent neonatal resection. Conversely, CLM with CVR < 1.1 may be safe to deliver at patient hospital of choice.