Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding

Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imaging data. In practice, experienced clinicians can have a preliminary assessment of patients' health status based on patients' observable physical appearances, which are mainly facial features. However, such assessment is highly subjective. In this work, the efficacy of objectively capturing and using prognostic information contained in conventional portrait photographs using deep learning for survival predication purposes is investigated for the first time. A pre-trained StyleGAN2 model is fine-tuned on a custom dataset of our cancer patients' photos to empower its generator with generative ability suitable for patients' photos. The StyleGAN2 is then used to embed the photographs to its highly expressive latent space. Utilizing the state-of-the-art survival analysis models and based on StyleGAN's latent space photo embeddings, this approach achieved a C-index of 0.677, which is notably higher than chance and evidencing the prognostic value embedded in simple 2D facial images. In addition, thanks to StyleGAN's interpretable latent space, our survival prediction model can be validated for relying on essential facial features, eliminating any biases from extraneous information like clothing or background. Moreover, a health attribute is obtained from regression coefficients, which has important potential value for patient care

Advanced Materials Technologies / 3D Printing of Hierarchical Porous Silica and -Quartz

The ability to macroscopically shape highly porous oxide materials while concomitantly tailoring the porous network structure as desired by simple and environmentally friendly processes is of great importance in many fields. Here, a purely aqueous printing process toward deliberately shaped, hierarchically organized amorphous silica and the corresponding polycrystalline quartz analogues based on a direct ink writing process (DIW) is presented. The key to success is the careful development of the solgel ink, which is based on an acidic aqueous sol of a glycolated silane and structuredirecting agents. The resulting 3D (DIW) printed silica consists of a macroporous network of struts comprising hexagonally arranged mesopores on a 2D hexagonal lattice. Together with a printed porous superstructure on the millimeter scale, welldefined pore sizes and shapes on at least three hierarchy levels can thus be fabricated. The introduction of devitrifying agents in the printed green part and subsequent heat treatment allows for the transformation of the silica structure into polycrystalline quartz. While small pores (micro and mesopores below 10 nm) are lost, the printed morphology and the macroporous network of struts is preserved during crystallization.1605N20(VLID)266643

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as false positive metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Our proposed VSS loss improves the sensitivity of brain metastasis detection for DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the false positive metastases are reduced in the high sensitivity model and the precision reaches 99.6% for the high specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high sensitivity and high specificity models, on average only 1.5 false positive metastases per patient needs further check, while the majority of true positive metastases are confirmed. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice.Comment: Implementation is available to public at https://github.com/YixingHuang/DeepMedicPlu

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal gray zone cases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates good knowledge of statistics, CNS & eye, pediatrics, biology, and physics but has limitations in bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs well in diagnosis, prognosis, and toxicity but lacks proficiency in topics related to brachytherapy and dosimetry, as well as in-depth questions from clinical trials. For the gray zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Most importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Because of the risk of hallucination, facts provided by ChatGPT always need to be verified

High-content drug screening in zebrafish xenografts reveals high efficacy of dual MCL-1/BCL-XL inhibition against Ewing sarcoma

Ewing sarcoma is a pediatric bone and soft tissue cancer with an urgent need for new therapies to improve disease outcome. To identify effective drugs, phenotypic drug screening has proven to be a powerful method, but achievable throughput in mouse xenografts, the preclinical Ewing sarcoma standard model, is limited. Here, we explored the use of xenografts in zebrafish for high-throughput drug screening to discover new combination therapies for Ewing sarcoma. We subjected xenografts in zebrafish larvae to high-content imaging and subsequent automated tumor size analysis to screen single agents and compound combinations. We identified three drug combinations effective against Ewing sarcoma cells: Irinotecan combined with either an MCL-1 or an BCL-XL inhibitor and in particular dual inhibition of the anti-apoptotic proteins MCL-1 and BCL-XL, which efficiently eradicated tumor cells in zebrafish xenografts. We confirmed enhanced efficacy of dual MCL-1/BCL-XL inhibition compared to single agents in a mouse PDX model. In conclusion, high-content screening of small compounds on Ewing sarcoma zebrafish xenografts identified dual MCL-1/BCL-XL targeting as a specific vulnerability and promising therapeutic strategy for Ewing sarcoma, which warrants further investigation towards clinical application. Keywords: Anti-apoptotic protein inhibitors; Ewing sarcoma; High-content imaging; Phenotypic drug screening; Zebrafish xenograft

A hypomorphic vasopressin allele prevents anxiety-related behavior

In this study, microarray analysis, in situ hybridization, quantitative real-time PCR and immunohistochemistry revealed decreased expression of the vasopressin gene (Avp) in the hypothalamic paraventricular (PVN) and supraoptic (SON) nuclei of adult LAB mice compared to HAB, NAB (normal anxiety-related behavior) and HABxLAB F1 intercross controls, without detecting differences in receptor expression or density. By sequencing the regions 2.5 kbp up- and downstream of the Avp gene locus, we could identify several polymorphic loci, differing between the HAB and LAB lines. In the gene promoter, a deletion of twelve bp Δ(−2180–2191) is particularly likely to contribute to the reduced Avp expression detected in LAB animals under basal conditions. Indeed, allele-specific transcription analysis of F1 animals revealed a hypomorphic LAB-specific Avp allele with a reduced transcription rate by 75% compared to the HAB-specific allele, thus explaining line-specific Avp expression profiles and phenotypic features. Accordingly, intra-PVN Avp mRNA levels were found to correlate with anxiety-related and depression-like behaviors. In addition to this correlative evidence, a significant, though moderate, genotype/phenotype association was demonstrated in 258 male mice of a freely-segregating F2 panel, suggesting a causal contribution of the Avp promoter deletion to anxiety-related behavior

Stereotactic or conformal radiotherapy for adrenal metastases: patient characteristics and outcomes in a multicenter analysis

To report outcome (freedom from local progression: FFLP, overall survival: OS, and toxicity) after stereotactic, palliative, or highly conformal fractionated (> 12) radiotherapy (SBRT, Pall-RT, 3DCRT/IMRT) for adrenal metastases in a retrospective multicenter cohort within the framework of the German Society for Radiation Oncology (DEGRO). Adrenal metastases treated with SBRT (≤ 12 fractions, biologically effective dose, (BED10) ≥ 50 Gy), 3DCRT/IMRT (> 12 fractions, BED10 ≥ 50 Gy) or Pall-RT (BED10 < 50 Gy) were eligible for this analysis. In addition to unadjusted FFLP (Kaplan-Meier/Log-rank), we calculated the competing-risk-adjusted local recurrence rate (CRA-LRR). 326 patients with 366 metastases were included by 21 centers (median follow-up: 11.7 months). Treatment was SBRT, 3DCRT/IMRT, and Pall-RT in 260, 27, and 79 cases, respectively. Most frequent primary tumors were non-small-cell lung cancer (NSCLC; 52.5%), SCLC (16.3%), and melanoma (6.7%). Unadjusted FFLP was higher after SBRT v. Pall-RT (p = 0.026) while numerical differences in CRA-LRR between groups did not reach statistical significance (1-year CRA-LRR: 13.8%, 17.4%, and 27.7%). OS was longer after SBRT v. other groups (p < 0.05) and increased in patients with locally-controlled metastases in a landmark analysis (p < 0.0001). Toxicity was mostly mild; notably, 4 cases of adrenal insufficiency occurred, 2 of which were likely caused by immunotherapy or tumor progression. RT for adrenal metastases was associated with a mild toxicity profile in all groups and a favorable 1-year CRA-LRR after SBRT or 3DCRT/IMRT. 1-year FFLP was associated with longer OS. Dose-response analyses for the dataset are underway

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology

PurposeThe potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.MethodsThe 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases.ResultsFor the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4’s strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts.ConclusionBoth evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy