Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful. METHODS: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN's classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists' accuracies according to their level of experience and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed an accuracy and ROC AUC with corresponding 95 % confidence intervals (CI) of 91.0 % (83.8 % to 95.2 %) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7 % (82.5 % to 84.8 %). With level II information, the accuracy improved to 87.8 % (86.7 % to 88.9 %; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN's accuracy was higher (91.0 % versus 87.8 %, p < 0.001). For experts with level II information results were on par with the CNN (91.0 % versus 90.4 %, p = 0.368). CONCLUSIONS: The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models.

Hope for motherhood: pregnancy after allogeneic hematopoietic cell transplantation (a national multicenter study).

ABSTRACT: Improved long-term survival rates after allogeneic hematopoietic cell transplantation (alloHCT) make family planning for young adult cancer survivors an important topic. However, treatment-related infertility risk poses challenges. To assess pregnancy and birth rates in a contemporary cohort, we conducted a national multicenter study using data from the German Transplant Registry, focusing on adult women aged 18 to 40 years who underwent alloHCT between 2003 and 2018. Of 2654 women who underwent transplantation, 50 women experienced 74 pregnancies, occurring at a median of 4.7 years after transplant. Fifty-seven of these resulted in live births (77%). The annual first birth rate among HCT recipients was 0.45%, which is >6 times lower than in the general population. The probability of a live birth 10 years after HCT was 3.4%. Factors associated with an increased likelihood of pregnancy were younger age at alloHCT, nonmalignant transplant indications, no total body irradiation or a cumulative dose of <8 Gy, and nonmyeloablative/reduced-intensity conditioning. Notably, 72% of pregnancies occurred spontaneously, with assisted reproductive technologies used in the remaining cases. Preterm delivery and low birth weight were more common than in the general population. This study represents the largest data set reporting pregnancies in a cohort of adult female alloHCT recipients. Our findings underscore a meaningful chance of pregnancy in alloHCT recipients. Assisted reproductive technologies techniques are important and funding should be made available. However, the potential for spontaneous pregnancies should not be underestimated, and patients should be informed of the possibility of unexpected pregnancy despite reduced fertility. Further research is warranted to understand the impact of conditioning decisions on fertility preservation.

Oligoclonal CD4+CXCR5+ T cells with a cytotoxic phenotype appear in tonsils and blood.

In clinical situations, peripheral blood accessible CD3+CD4+CXCR5+ T-follicular helper (TFH) cells may have to serve as a surrogate indicator for dysregulated germinal center responses in tissues. To determine the heterogeneity of TFH cells in peripheral blood versus tonsils, CD3+CD4+CD45RA-CXCR5+ cells of both origins were sorted. Transcriptomes, TCR repertoires and cell-surface protein expression were analysed by single-cell RNA sequencing, flow cytometry and immunohistochemistry. Reassuringly, all blood-circulating CD3+CD4+CXCR5+ T-cell subpopulations also appear in tonsils, there with some supplementary TFH characteristics, while peripheral blood-derived TFH cells display markers of proliferation and migration. Three further subsets of TFH cells, however, with bona fide T-follicular gene expression patterns, are exclusively found in tonsils. One additional, distinct and oligoclonal CD4+CXCR5+ subpopulation presents pronounced cytotoxic properties. Those 'killer TFH (TFK) cells' can be discovered in peripheral blood as well as among tonsillar cells but are located predominantly outside of germinal centers. They appear terminally differentiated and can be distinguished from all other TFH subsets by expression of NKG7 (TIA-1), granzymes, perforin, CCL5, CCR5, EOMES, CRTAM and CX3CR1. All in all, this study provides data for detailed CD4+CXCR5+ T-cell assessment of clinically available blood samples and extrapolation possibilities to their tonsil counterparts.

Haploidentical transplantation in primary refractory/relapsed secondary vs de novo AML: from the ALWP/EBMT.

ABSTRACT: We compared the outcomes of haploidentical stem cell transplantation (haplo-HSCT) with posttransplant cyclophosphamide (PTCy) in 719 patients with primary refractory (PR) or first relapse (Rel) secondary acute myeloid leukemia (sAML; n = 129) vs those with de novo AML (n = 590), who received HSCT between 2010 and 2022. A higher percentage of patients with sAML vs de novo AML had PR disease (73.6% vs 58.6%; P = .002). In 81.4% of patients with sAML , the antecedent hematological disorder was myelodysplastic syndrome. Engraftment was 83.5% vs 88.4% in sAML and de novo AML, respectively (P = .13). In multivariate analysis, haplo-HSCT outcomes did not differ significantly between the groups: nonrelapse mortality hazard ratio (HR), 1.38 (95% confidence interval [CI], 0.96-1.98; P = .083), relapse incidence HR, 0.68 (95% CI, 0.4.7.-1.00; P = .051). The HRs for leukemia-free survival, overall survival, and graft-versus-host disease (GVHD)-free, and GVHD and relapse-free survival were 0.99 (95% CI, 0.76-1.28; P = .94), 0.99 (95% CI, 0.77-1.29; P = .97), and 0.99 (95% CI, 0.77-1.27; P = .94), respectively. We conclude that outcomes of haplo-HSCT with PTCy are not different for PR/Rel sAML in comparison with PR/Rel de novo AML, a finding of major clinical importance.

Adoptive transfer of donor B lymphocytes: a phase 1/2a study for patients after allogeneic stem cell transplantation.

ABSTRACT: Immune reconstitution after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is slow and patients carry a high and prolonged risk of opportunistic infections. We hypothesized that the adoptive transfer of donor B cells can foster after HSCT immuno-reconstitution. Here, we report, to our knowledge, the results of a first-in-human phase 1/2a study aimed to evaluate the feasibility and safety of adoptively transferred donor B cells and to test their activity upon recall vaccination. Good manufactoring practice (GMP) B-cell products were generated from donor apheresis products using 2-step magnetic cell separation. Fifteen patients who had undergone allo-HSCT were enrolled and treated after taper of immunosuppression (median, day +148; range, 130-160). Patients received 4 different doses of B cells (0.5 × 106 to 4.0 × 106 B cells per kg body weight). To test the activity of infused donor memory B cells in vivo, patients were vaccinated with a pentavalent vaccine 7 days after B-cell transfer. We observed the mobilization of plasmablasts and an increase in serum titers against vaccine antigens, with a stronger response in patients receiving higher B-cell numbers. Analysis of immunoglobulin VH-sequences by next-generation sequencing revealed that plasmablasts responding to vaccination originated from memory B-cell clones from the donor. Donor B-cell transfer was safe, as no Epstein-Barr virus (EBV) reactivation was observed, and only low-grade graft-versus-host disease (GVHD) occurred in 4 out of 15 patients. This pilot trial may pave the way for further studies exploring the adoptive transfer of memory B cells to reduce the frequency of infections after allo-HSCT. This trial was registered at ClinicalTrial.gov as #NCT02007811.

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance.

IMPORTANCE: Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS: In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS: At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS: ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.

Systemic exposure to aflibercept after intravitreal injection in premature neonates with retinopathy of prematurity: results from the FIREFLEYE randomized phase 3 study.

BACKGROUND: There are no data on pharmacokinetics, pharmacodynamics, and immunogenicity of intravitreal aflibercept in preterm infants with retinopathy of prematurity (ROP). FIREFLEYE compared aflibercept 0.4 mg/eye and laser photocoagulation in infants with acute-phase ROP requiring treatment. METHODS: Infants (gestational age ≤32 weeks or birthweight ≤1500 g) with treatment-requiring ROP in ≥1 eye were randomized 2:1 to receive aflibercept 0.4 mg or laser photocoagulation at baseline in this 24-week, randomized, open-label, noninferiority, phase 3 study. Endpoints include concentrations of free and adjusted bound aflibercept in plasma, pharmacokinetic/pharmacodynamic exploration of systemic anti-vascular endothelial growth factor effects, and immunogenicity. RESULTS: Of 113 treated infants, 75 received aflibercept 0.4 mg per eye at baseline (mean chronological age: 10.4 weeks), mostly bilaterally (71 infants), and with 1 injection/eye (120/146 eyes). Concentrations of free aflibercept were highly variable, with maximum concentration at day 1, declining thereafter. Plasma concentrations of adjusted bound (pharmacologically inactive) aflibercept increased from day 1 to week 4, decreasing up to week 24. Six infants experienced treatment-emergent serious adverse events within 30 days of treatment; aflibercept concentrations were within the range observed in other infants. There was no pattern between free and adjusted bound aflibercept concentrations and blood pressure changes up to week 4. A low-titer (1:30), non-neutralizing, treatment-emergent anti-drug antibody response was reported in 1 infant, though was not clinically relevant. CONCLUSIONS: 24-week data suggest intravitreal aflibercept for treatment of acute-phase ROP is not associated with clinically relevant effects on blood pressure, further systemic adverse events, or immunogenicity. GOV IDENTIFIER: NCT04004208.

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.

Importance: Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking. Objective: To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions. Design, Setting, and Participants: In this prospective diagnostic 2-center study, dermatologists performed skin cancer screenings using naked-eye examination and dermoscopy. Dermatologists graded suspect melanocytic lesions by the probability of malignancy (range 0-1, threshold for malignancy ≥0.5) and indicated management decisions (no action, follow-up, excision). Next, dermoscopic images of suspect lesions were assessed by a market-approved CNN, Moleanalyzer Pro (FotoFinder Systems). The CNN malignancy scores (range 0-1, threshold for malignancy ≥0.5) were transferred to dermatologists with the request to re-evaluate lesions and revise initial decisions in consideration of CNN results. Reference diagnoses were based on histopathologic examination in 125 (54.8%) lesions or, in the case of nonexcised lesions, on clinical follow-up data and expert consensus. Data were collected from October 2020 to October 2021. Main Outcomes and Measures: Primary outcome measures were diagnostic sensitivity and specificity of dermatologists alone and dermatologists cooperating with the CNN. Accuracy and receiver operator characteristic area under the curve (ROC AUC) were considered as additional measures. Results: A total of 22 dermatologists detected 228 suspect melanocytic lesions (190 nevi, 38 melanomas) in 188 patients (mean [range] age, 53.4 [19-91] years; 97 [51.6%] male patients). Diagnostic sensitivity and specificity significantly improved when dermatologists additionally integrated CNN results into decision-making (mean sensitivity from 84.2% [95% CI, 69.6%-92.6%] to 100.0% [95% CI, 90.8%-100.0%]; P = .03; mean specificity from 72.1% [95% CI, 65.3%-78.0%] to 83.7% [95% CI, 77.8%-88.3%]; P < .001; mean accuracy from 74.1% [95% CI, 68.1%-79.4%] to 86.4% [95% CI, 81.3%-90.3%]; P < .001; and mean ROC AUC from 0.895 [95% CI, 0.836-0.954] to 0.968 [95% CI, 0.948-0.988]; P = .005). In addition, the CNN alone achieved a comparable sensitivity, higher specificity, and higher diagnostic accuracy compared with dermatologists alone in classifying melanocytic lesions. Moreover, unnecessary excisions of benign nevi were reduced by 19.2%, from 104 (54.7%) of 190 benign nevi to 84 nevi when dermatologists cooperated with the CNN (P < .001). Most lesions were examined by dermatologists with 2 to 5 years (96, 42.1%) or less than 2 years of experience (78, 34.2%); others (54, 23.7%) were evaluated by dermatologists with more than 5 years of experience. Dermatologists with less dermoscopy experience cooperating with the CNN had the most diagnostic improvement compared with more experienced dermatologists. Conclusions and Relevance: In this prospective diagnostic study, these findings suggest that dermatologists may improve their performance when they cooperate with the market-approved CNN and that a broader application of this human with machine approach could be beneficial for dermatologists and patients.

Round-Robin test for the histological diagnosis of acute colonic Graft-versus-Host disease validating established histological criteria and grading systems.

Histomorpholgy is one of the mainstays of acute Graft-versus-host disease (GvHD) diagnosis. However, concerns about reproducibility and the most appropriate grading system question its usefulness. Our aim was to assess histomorphological parameters and previously reported grading systems for GvHD regarding reproducibility and validity. Moreover, we propose that sum scores, derived by combining separately scored morphological parameters into a total score, might provide a simplified but equally effective means to grade GvHD. A total of 123 colon biopsies were assessed across four pathologists for intestinal GvHD using a Round-Robin test and results were correlated with clinical findings. Interobserver reproducibility was high for histological parameters that were evaluated as indicators of acute GvHD. Published grading systems were moderately reproducible (ICC 0.679-0.769) while simplified sum scores, in comparison, showed better interrater reliability (ICC 0.818-0.896). All grading systems and sum scores were associated with clinical signs of GvHD and in part with therapy response and survival. However, they were not able to stratify patients according to the clinical severity of GvHD. In a hot-spot analysis 1 crypt apoptotic body (CAB) in 10 crypts was a reasonable cut-off value for minimal diagnostic criteria of GvHD. In conclusion, histology can contribute to the diagnosis of GvHD and is reproducible. Published grading systems are able to reflect clinical findings as are simplified sum scores, which showed improved reproducibility and might be easier to handle as they are based on adding up histological parameters rather than transferring histological findings into a separate grading system. Sum scores will have to be further tested in a prospective setting.

Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.

BACKGROUND: The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN). METHODS: In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images. RESULTS: After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01). CONCLUSIONS: Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.

UV Irradiation of Nevi: Impact on Performance of Electrical Impedance Spectroscopy and a Convolution Neural Network.

Introduction: UV irradiation of nevi induces transient melanocytic activation with dermoscopic and histological changes. Objectives: We investigated whether UV irradiation of nevi may influence electrical impedance spectroscopy (EIS) or convolution neural networks (CNN). Methods: Prospective, controlled trial in 50 patients undergoing phototherapy (selective UV phototherapy (SUP), UVA1, SUP/UVA1, or PUVA). EIS (Nevisense, SciBase AB) and CNN scores (Moleanalyzer-Pro, FotoFinder Systems) of nevi were assessed before (V1) and after UV irradiation (V2). One nevus (nevusirr) was exposed to UV light, another UV-shielded (nevusnon-irr). Results: There were no significant differences in EIS scores of nevusirr before (2.99 [2.51-3.47]) and after irradiation (3.32 [2.86-3.78]; P = 0.163), which was on average 13.28 (range 4-47) days later. Similarly, UV-shielded nevusnon-irr did not show significant changes of EIS scores (V1: 2.65 [2.19-3.11]), V2: 2.92 [2.50-3.34]; P = 0.094). Subgroup analysis by irradiation revealed a significant increase of EIS scores of nevusirr (V1: 2.69 [2.21-3.16], V2: 3.23 [2.72-3.73]; P = 0.044) and nevusnon-irr (V1: 2.57 [2.07-3.07], V2: 3.03 [2.48-3.57]; P = 0.033) for patients receiving SUP. In contrast, CNN scores of nevusirr (P = 0.995) and nevusnon-irr (P = 0.352) showed no significant differences before and after phototherapy. Conclusions: For the tested EIS system increased EIS scores were found in nevi exposed to SUP. In contrast, CNN results were more robust against UV exposure.

[Artificial intelligence-based classification for the diagnostics of skin cancer]. / Bilderkennung mittels künstlicher Intelligenz in der Hautkrebsdiagnostik.

Convolutional neural networks (CNN) achieve a level of performance comparable or even superior to dermatologists in the assessment of pigmented and nonpigmented skin lesions. In the analysis of images by artificial neural networks, images on a pixel level pass through various layers of the network with different graphic filters. Based on excellent study results, a first deep learning network (Moleanalyzer pro, Fotofinder Systems GmBH, Bad Birnbach, Germany) received market approval in Europe. However, such neural networks also reveal relevant limitations, whereby rare entities with insufficient training images are classified less adequately and image artifacts can lead to false diagnoses. Best results can ultimately be achieved in a cooperation of "man with machine". For future skin cancer screening, automated total body mapping is evaluated, which combines total body photography, automated data extraction and assessment of all relevant skin lesions.

Metabolic Signature of Atypical Fibroxanthoma and Pleomorphic Dermal Sarcoma: Expression of Hypoxia-inducible Factor-1α and Several of Its Downstream Targets.

Metabolic reprogramming mediated by hypoxia-inducible factors play a crucial role in many human cancers. HIF-1α is activated under hypoxic conditions and is considered a key regulator of oxygen homoeostasis during tumor proliferation under hypoxia. Aim of this research was to analyze the immunohistochemical expression of HIF-1α, VEGF-A, Glut-1, MCT4, and CAIX in atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS). 21 paraffin-embedded AFX and 22 PDS were analysed by immunohistochemistry, namely HIF-1α, VEGF-A (referred to as VEGF throughout the manuscript), Glut-1, MCT4, and CAIX. To quantify the protein expression, we considered the percentage of positive tumor cells (0: 0%, 1: up to 1%, 2: 2-10%, 3: 11-50%, 4: >50%) in relation to the staining intensity (0: negative, 1: low, 2: medium, 3: strong). HIF-1α expression (mean ± SD) in AFX (9.33±2.92) was significantly stronger than that in PDS (5.90±4.38; P= 0.007), whereas the expression of VEGF, Glut-1, MCT4, and CAIX did not show differences between AFX and PDS. When comparing all tumors without subgroup stratification, the expression of HIF-1α (P= 0.044) and MCT4 (P= 0.036) was significantly stronger in ulcerated tumors than in tumors without ulceration. Our findings provide the first evidence that HIF-1α-induced metabolic reprogramming may contribute to the pathogenesis of AFX and PDS. HIF-1α expression seems to be higher in AFX than in PDS, and ulcerated tumors show higher expression levels of HIF-1α and MCT4 irrespective of the diagnosis.

Does sex matter? Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection.

BACKGROUND: Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany). METHODS: We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776). RESULTS: Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59). CONCLUSION: Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions.

[Immunohistochemical analysis of a hypoxia-associated signature in melanomas with positive and negative sentinel lymph nodes : Hypoxia-associated signature of primary cutaneous melanomas]. / Immunhistochemische Analyse einer Hypoxie-assoziierten Signatur in Melanomen mit positivem und negativem Schildwächterlymphknoten : Hypoxie-assoziierte Signatur primär kutaner Melanome.

Metabolic reprogramming mediated by hypoxia-inducible factors and its downstream targets plays a crucial role in many human malignancies. Excessive proliferation of tumor cells under hypoxic conditions leads to metabolic reprogramming and altered gene expression enabling tumors to adapt to their hypoxic environment. Here we analyzed the metabolic signatures of primary cutaneous melanomas with positive and negative sentinel node status in order to evaluate potential differences in their metabolic signature. We found a positive correlation of the expression of glucose transporter 1 (GLUT-1) with tumor thickness and ulceration in all melanomas with subgroup analyses as well as in the subgroup with a negative sentinel node. Furthermore, the expression of vascular endothelial growth factor (VEGF) was positively correlated with the presence of ulceration in melanomas with positive sentinel node.

Monitoring patients at risk for melanoma: May convolutional neural networks replace the strategy of sequential digital dermoscopy?

BACKGROUND: Sequential digital dermoscopy (SDD) is applied for early melanoma detection by uncovering dynamic changes of monitored lesions. Convolutional neural networks (CNN) are capable of high diagnostic accuracies similar to trained dermatologists. OBJECTIVES: To investigate the capability of CNN to correctly classify melanomas originally diagnosed by mere dynamic changes during SDD. METHODS: A retrospective cross-sectional study using image quartets of 59 high-risk patients each containing one melanoma diagnosed by dynamic changes during SDD and three nevi (236 lesions). Two validated CNN classified quartets at baseline or after SDD follow-up at the time of melanoma diagnosis. Moreover, baseline quartets were rated by 26 dermatologists. The main outcome was the number of quartets with correct classifications. RESULTS: CNN-1 correctly classified 9 (15.3%) and CNN-2 8 (13.6%) of 59 baseline quartets. In baseline images, CNN-1 attained a sensitivity of 25.4% (16.1%-37.8%) and specificity of 92.7% (87.8%-95.7%), whereas CNN-2 of 28.8% (18.8%-41.4%) and 75.7% (68.9%-81.4%). Expectedly, after SDD follow-up CNN more readily detected melanomas resulting in improved sensitivities (CNN-1: 44.1% [32.2%-56.7%]; CNN-2: 49.2% [36.8%-61.6%]). Dermatologists were told that each baseline quartet contained one melanoma, and on average, correctly classified 24 (22-27) of 59 quartets. Correspondingly, accepting a baseline quartet to be appropriately classified whenever the highest malignancy score was assigned to the melanoma within, CNN-1 and CNN-2 correctly classified 28 (47.5%) and 22 (37.3%) of 59 quartets, respectively. CONCLUSIONS: The tested CNN could not replace the strategy of SDD. There is a need for CNN capable of integrating information on dynamic changes into analyses.

Population Pharmacokinetics of Ipatasertib and Its Metabolite in Cancer Patients.

Ipatasertib is a selective AKT kinase inhibitor currently in development for the treatment of several solid tumors, including breast and prostate cancers. This study was undertaken to characterize pharmacokinetic profiles of ipatasertib and its metabolite M1 (G-037720) and to understand the sources of variability. Population pharmacokinetic models of ipatasertib and M1 were developed separately using data from 342 individuals with cancer from 5 phase 1 and 2 studies. The final population pharmacokinetic models for ipatasertib and M1 were 3-compartmental, with first-order elimination and sequential zero- and first-order absorption. Ipatasertib bioavailability and M1 formation increased after multiple dosing, resulting in an increase in exposure beyond that expected from accumulation alone. Covariate effects of ipatasertib include decreased oral clearance with increasing age and with coadministration of abiraterone, as well as decreased bioavailability with increasing weight. For ages 37 and 80 years, steady-state area under the curve (AUCss ) was predicted to be 81% and 109%, respectively, of the typical population value (64 years). For body weight of 49 and 111 kg, AUCss was predicted to be 132% and 78%, respectively, of the typical population value (75 kg). The small magnitude of change in ipatasertib exposure is not likely to be clinically relevant. For M1, the peripheral distribution volume and intercompartmental clearance increased with increasing weight. Coadministration of abiraterone was estimated to increase M1 exposure by 61% at steady state. Mild and moderate renal impairment, mild hepatic impairment, and race were not identified as significant covariates in the final models for ipatasertib and M1.

Collective human intelligence outperforms artificial intelligence in a skin lesion classification task.

BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNN) enable accurate diagnosis of medical images and perform on or above the level of individual physicians. Recently, collective human intelligence (CoHI) was shown to exceed the diagnostic accuracy of individuals. Thus, diagnostic performance of CoHI (120 dermatologists) versus individual dermatologists versus two state-of-the-art CNN was investigated. PATIENTS AND METHODS: Cross-sectional reader study with presentation of 30 clinical cases to 120 dermatologists. Six diagnoses were offered and votes collected via remote voting devices (quizzbox®, Quizzbox Solutions GmbH, Stuttgart, Germany). Dermatoscopic images were classified by a binary and multiclass CNN (FotoFinder Systems GmbH, Bad Birnbach, Germany). Three sets of diagnostic classifications were scored against ground truth: (1) CoHI, (2) individual dermatologists, and (3) CNN. RESULTS: CoHI attained a significantly higher accuracy [95 % confidence interval] (80.0 % [62.7 %-90.5 %]) than individual dermatologists (75.7 % [73.8 %-77.5 %]) and CNN (70.0 % [52.1 %-83.3 %]; all P < 0.001) in binary classifications. Moreover, CoHI achieved a higher sensitivity (82.4 % [59.0 %-93.8 %]) and specificity (76.9 % [49.7 %-91.8 %]) than individual dermatologists (sensitivity 77.8 % [75.3 %-80.2 %], specificity 73.0 % [70.6 %-75.4 %]) and CNN (sensitivity 70.6 % [46.9 %-86.7 %], specificity 69.2 % [42.4 %-87.3 %]). The diagnostic accuracy of CoHI was superior to that of individual dermatologists (P < 0.001) in multiclass evaluation, with the accuracy of the latter comparable to multiclass CNN. CONCLUSIONS: Our analysis revealed that the majority vote of an interconnected group of dermatologists (CoHI) outperformed individuals and CNN in a demanding skin lesion classification task.