United States

Background: Osteosarcoma is the most common primary bone tumor in children and adolescents. Prognostic biomarkers are limited, and post-treatment surveillance relies heavily on radiologic imaging. Capture and analysis of cell-free DNA (cfDNA) from the bloodstream is an emerging technique that could be used to provide additional prognostic information and track residual disease following treatment. 5-hydroxymethylcytosine (5-hmC) is an epigenetic marker of open chromatin and active gene expression, and tumor specific patterns of global 5-hmC deposition are identifiable in several cancers and vary according to disease burden. We profiled 5-hmC using plasma-derived cfDNA from osteosarcoma patients and healthy children to assess its utility as a biomarker of disease status. 

Methods: cfDNA was isolated from patients diagnosed with osteosarcoma and well child controls. Nano-hmC-Seal, a technique developed to isolate 5-hmC-modified DNA fragments, was used to generate sequencing libraries of cfDNA. Genomic DNA from osteosarcoma cell lines was serially diluted with healthy donor-derived cfDNA. DESeq2 was used to identify genes with high 5-hmC deposition in osteosarcoma cell line DNA relative to serial dilutions. In patient cfDNA samples, the relative abundance of 5-hmC deposition on these osteosarcoma genes was determined using gene set variation analysis (GSVA). GSVA returns a score ranging from -1 to 1, with positive values indicating a relative abundance of 5-hmC deposition on osteosarcoma genes.

Results: The cohort consisted of 33 healthy children and 17 patients with osteosarcoma including 53 serially collected samples from these patients. 139 osteosarcoma genes were identified and used for GSVA analysis. Patients with osteosarcoma had significantly higher GSVA scores than well children (0.26 vs -0.38; p&lt;0.001). Hierarchal clustering identified two clusters of samples from children with osteosarcoma. Cluster 1 contained samples with scores correlating with osteosarcoma-derived DNA compared to Cluster 2 (0.14 vs -0.18; p&lt;0.001). Furthermore, Cluster 1 contained 17 of 23 samples from patients with active disease on clinical imaging (sensitivity = 74%, specificity = 57%). High 5-hmC deposition on MYC was readily identified in one sample obtained at diagnosis from the only patient with a MYC amplified tumor. Additional samples from this patient during treatment showed decreasing 5-hmC deposition on MYC, consistent with a treated tumor shedding less DNA into the blood. Additionally, decreased disease burden following surgical resection was identifiable in 5-hmC profiles (presurgical GSVA score 0.22 vs postsurgical -0.08; p=0.004). 

Conclusions: Osteosarcoma 5-hmC signatures can identify patients with active disease. Monitoring cfDNA in osteosarcoma using 5-hmC profiling is a promising technique that warrants further investigation with larger patient cohorts.


AMA Research Challenge 2023

Identifying osteosarcoma signatures in cell free DNA using 5-hydroxymethylcytosine profiling

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) is held fully online. We have an exciting program consisting of keynote talks by [Susan Murphy](https://www.auai.org/uai2021/program?block=A), [Eric Horvitz](https://www.auai.org/uai2021/program?block=B), [Lenka Zdeborová](https://www.auai.org/uai2021/program?block=C), [Judea Pearl](https://www.auai.org/uai2021/program?block=D) and [Ankur Moitra](https://www.auai.org/uai2021/program?block=E), a town hall meeting, peer-reviewed papers, and three workshops. The main conference is single-track and each paper is show-cased with a live component (either a live discussion and Q&A, or a live lightning talk). All papers are also featured in video presentations and in poster sessions in gather.town. The three parallel workshops on Friday allow for smaller meetings on current topics.

The walkthrough video below shows how you can navigate this site and use the different possibilities for interaction: UAIsland2021 on gather.town for posters and social interactions, the mementor platform for virtual mentoring sessions, and the chat and Q&A systems for discussions about talks and posters.

Have a great UAI 2021! 
 
 Cassio de Campos and Marloes Maathuis
 UAI 2021 Program Chairs
 uai2021programchairs@gmail.com

<figure class="video_container">
 <iframe src="https://screencast-o-matic.com/watch/cri0lsViAGK?v=6&ff=1&title=0&controls=1" width=526 height=226 frameborder="0" allowfullscreen="true"> </iframe>
</figure>

A ticket is required to access this conference. Please use the provided link to register for the event.

https://auai.org/uai2021/registration

UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

Background: Osteosarcoma is the most common primary bone tumor in children and adolescents. Prognostic biomarkers are limited, and post-treatment surveillance relies heavily on radiologic imaging. Capture and analysis of cell-free DNA (cfDNA) from the bloodstream is an emerging technique that could be used to provide additional prognostic information and track residual disease following treatment. 5-hydroxymethylcytosine (5-hmC) is an epigenetic marker of open chromatin and active gene expression, and tumor specific patterns of global 5-hmC deposition are identifiable in several cancers and vary according to disease burden. We profiled 5-hmC using plasma-derived cfDNA from osteosarcoma patients and healthy children to assess its utility as a biomarker of disease status. 

Methods: cfDNA was isolated from patients diagnosed with osteosarcoma and well child controls. Nano-hmC-Seal, a technique developed to isolate 5-hmC-modified DNA fragments, was used to generate sequencing libraries of cfDNA. Genomic DNA from osteosarcoma cell lines was serially diluted with healthy donor-derived cfDNA. DESeq2 was used to identify genes with high 5-hmC deposition in osteosarcoma cell line DNA relative to serial dilutions. In patient cfDNA samples, the relative abundance of 5-hmC deposition on these osteosarcoma genes was determined using gene set variation analysis (GSVA). GSVA returns a score ranging from -1 to 1, with positive values indicating a relative abundance of 5-hmC deposition on osteosarcoma genes.

Results: The cohort consisted of 33 healthy children and 17 patients with osteosarcoma including 53 serially collected samples from these patients. 139 osteosarcoma genes were identified and used for GSVA analysis. Patients with osteosarcoma had significantly higher GSVA scores than well children (0.26 vs -0.38; p<0.001). Hierarchal clustering identified two clusters of samples from children with osteosarcoma. Cluster 1 contained samples with scores correlating with osteosarcoma-derived DNA compared to Cluster 2 (0.14 vs -0.18; p<0.001). Furthermore, Cluster 1 contained 17 of 23 samples from patients with active disease on clinical imaging (sensitivity = 74%, specificity = 57%). High 5-hmC deposition on MYC was readily identified in one sample obtained at diagnosis from the only patient with a MYC amplified tumor. Additional samples from this patient during treatment showed decreasing 5-hmC deposition on MYC, consistent with a treated tumor shedding less DNA into the blood. Additionally, decreased disease burden following surgical resection was identifiable in 5-hmC profiles (presurgical GSVA score 0.22 vs postsurgical -0.08; p=0.004). 

Conclusions: Osteosarcoma 5-hmC signatures can identify patients with active disease. Monitoring cfDNA in osteosarcoma using 5-hmC profiling is a promising technique that warrants further investigation with larger patient cohorts.

poster

Welcome to the **largest national, multi-specialty research** event featuring research from nearly 700 medical students, residents, fellows, and international medical graduates.

Use the buttons below or navigation menu to access the event.
 


[![](https://assets.underline.io/uploads/markdown_image/1/image/3e33a8c754cc49610b30950617d813e7.png)](https://underline.io/events/434/sessions?searchGroup=all)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/551404d429c4e274f9a54cbdd7081918.png)](https://underline.io/events/434/posters?searchGroup=all)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/677d92f566b880cebd36905fc738fc10.png)](https://underline.io/events/434/lounge)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/c7392833c7261c3874c5b43fa2f4df19.png)](https://underline.io/events/434/help-desk)
 

#### Event Details

The 2023 American Medical Association Research Challenge includes a poster symposium hall and a semifinals hall with the top scored research. **Log in at any time throughout the event to:**

* **View posters and presentations** on a variety of topics and specialties
* **Score posters in the semifinals** to help decide the top five finalists (AMA member exclusive opportunity)
* **Connect with participants** and provide feedback to help advance their research
* **Attend education sessions** with physician experts on key research topics

At the conclusion of this event, the AMA will announce the top five finalists who will advance to the AMA Research Challenge finals, present their research to an elite panel of judges, and compete to win a $10,000 grand prize sponsored by Laurel Road. 

### Grand Prize Sponsor

AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in the judging or scoring process to select the winner. The winner will be announced on February 4, 2024 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

![](https://assets.underline.io/uploads/markdown_image/1/image/611e331d4a0eba0920f240cfc04b4d77.png)

**There is no registration fee for this event! **

Register to explore research in a variety of topics. AMA members get exclusive access to score posters in the semifinals.

**Please use this link [here](https://www.ama-assn.org/about/events/ama-research-challenge) to register for free.**

Registration Is Required

Join the AMA Research Challenge poster symposium and semifinals to explore top research posters, score posters in the semifinals (an AMA member exclusive opportunity) and attend education sessions with experienced physicians to learn tips to advance your research and career. 

Neuropilin 2 (NRP2) expression in tissue is an independent prognostic factor for aggressive prostate cancer. To investigate if this biomarker is also prognostic when measured using a less invasive sampling method, NRP2 and its ligand, vascular endothelial growth factor C (VEGF-C), were quantified using enzyme-linked immunoassay in 145 prostate cancer patient plasma samples and 15 controls from an opportunistic biobank. Statistical analysis investigated NRP2 and VEGF-C level associations with disease progression, tumor stage, Gleason score, and prostate-specific antigen (PSA). Plasma NRP2 was not associated with more aggressive disease, in contrast to what has been shown in tissue. Increased plasma VEGF-C was associated with less severe disease, including remission, Stage I/II, and intermediate-risk Gleason score. Further studies are needed to identify non-invasive methods to assess NRP2 status.

Association of plasma NRP2 and VEGF-C levels with prostate cancer disease severity

**Scoring is only possible in the semifinals hall**

**Abstract:**



Rare Presentation of HPV-related Retroperitoneal Squamous Cell Carcinoma of Unknown Primary: A case report

Background
Both hepatocellular cancer and liver metastases are associated with poor clinical outcome and limited therapeutic response to immunotherapy. Tumor antigen specific CD8 T cells in the liver have been studied in the setting of both immunotherapy and cellular therapy with the predominant dogma that these cells have equivalent functionality in the liver as compared to other organ sites. Herein, we detail that tumor antigen specific or potentially tumor reactive CD 8 T cells are uniquely nonfunctional in the liver immune microenvironment in both mice and humans. 
Methods
Both a colon cancer line (CT26) and a melanoma model (B16F10) were injected intrahepatically, into the tail vein, and subcutaneously in BALB/c and C57BL/6 mice to create liver, pulmonary and subcutaneous tumors respectively. CD8 T cells were depleted by ⍺CD8 treatment. Liver and tumor infiltrating immune cells and splenocytes were analyzed by flow cytometry. Peptide loaded tetramers were used to identify antigen-specific T cells and tumor growth was monitored. Additionally, publically available single cell RNA sequencing data from 10 patient samples with primary colon cancer and liver metastasis were used. Human single cell data was analyzed using the Seurat pipeline and single cell gene enrichment analysis in R. 
Results
Both in mice and humans, a higher frequency of tumor antigen specific or potentially tumor reactive CD8 T cells was found in the liver as compared to other organ sites. Additionally, a higher frequency of these were found to be exhausted in the liver (TIM3+ and CD39+ in mice and an exhausted cluster similarly defined in humans). Surprisingly, CD8 T cell depletion had no effect on tumor growth in the liver while depletion caused tumor growth in the subcutaneous model. Additionally, through Cell Chat analysis, SPP1 positive macrophages were found to be key mediators of this exhausted phenotype and these interactions were enriched in the liver. 
Conclusion
Tumor reactive CD8 T cells are found at significantly higher frequency in the liver. Despite the higher frequency of “killer” T cells in the liver, tumor growth in the liver was not impacted by tumor reactive CD8 T cells in contrast to subcutaneous tumors, which were smaller in the presence of tumor-specific T cells. Spp1+ macrophages seem to be key mediators in tumor reactive CD8 T cell dysfunction uniquely in the liver. These findings may explain the poor clinical response seen in these patients as well as present as a potentially target for therapeutic affects.

Macrophage-Mediated Hepatic Dysfunction Impairs Tumor Antigen-Specific CD8+ T Cell Function in the Liver

Background: Chronic Lymphocytic Leukemia (CLL) is a very common hematological malignancy, with an expected incidence of 18,740 cases in 2023, which makes up approximately 1% of all new cancer diagnoses. Cytogenetic (CG) characteristics play a critical role in the prognostic outcome and therapy of CLL. As per the National Comprehensive Cancer Network (NCCN), CLL prognosis has been categorized into 3 groups: favorable, intermediate, and unfavorable based on FISH abnormalities. The older staging and classification methods for CLL are inferior when compared to the new classification that incorporates cytogenetic analysis. The incidence of cytogenetic abnormalities is unknown in African-American populations. 

Methods: Records of adult patients with CLL were reviewed at Brooklyn Cancer Care, NY, from 2012 through 2023. The cytogenetic data were stratified into three prognostic groups; favorable: del (13q), del (11q); intermediate: normal cytogenetics, trisomy 12; and Unfavorable: del (17p), multiple cytogenetic abnormalities. In the general population, the incidence of CG abnormalities is as follows: del (13q) (55%), del (11q) (18%), trisomy 12 (16%), del (17p) (7%), and del (6q) (7%). Based on cytogenetic abnormalities found by FISH, IGHV mutation status, and CD38 expression, the Integrated CLL Scoring System (ICSS) puts patients into one of three risk groups: low, intermediate, or high. 

Results: Our study included 19 CLL patients. Among them, 42.1% (n = 8) had del (13q), 31.5% (n = 6) had trisomy 12, 10.5% (n = 2) had multiple CG abnormalities, and none of them had del (11q) or del (17p). According to the ICSS scoring system, 42.1% of the patients (n = 8) had a favorable prognosis, 47.3% (n = 9) had an intermediate prognosis, and 10.5% (n = 2) had an unfavorable prognosis. 

Conclusion: The incidence of CG abnormalities in CLL patients of African-American descent remains unknown. In our small study of 19 patients, we observed a lower incidence of the 13q mutation and a higher incidence of trisomy 12 among African-American patients compared to the general population. Del 17p was not present in any of our patients, while multiple cytogenetic abnormalities were observed. In conclusion, African American patients exhibited a higher proportion of intermediate and high-risk cytogenetics and a lower incidence of low-risk cytogenetics compared to the general population.

Incidence of Cytogenetic Abnormalities in CLL Patients of African American Origin in New York City

Background
Chat GPT is a natural language processing model that has been evaluated and used in a variety of different applications. However, its ability to provide references for the appropriate use of immunohistochemistry staining in dermatopathology has not been evaluated.

Methods
Both ChatGPT v3.5 and v4.0 were evaluated. Data collection occurred from May through June 2023. A total of 33 diagnoses involving benign growths, carcinomas, benign and malignant adnexal tumors, and cutaneous lymphomas were queried. For each condition or differential, ChatGPT was queried with “I am a dermatopathologist trying to work up a skin biopsy case with immunohistochemistry. I suspect the diagnosis to be _. Please supply a list of references that I can use to evaluate the utility of specific immunohistochemical stains for this diagnosis.” For ChatGPT v3.5, each prompt was regenerated 10 times; for ChatGPT v4.0, each prompt was regenerated 5 times. The outputs were recorded and compiled.

Results
ChatGPT v3.5 rarely provided clinically useful citations (15.0%), and over half were confabulated (51.5%). In ChatGPT v4.0, performance was improved with a greater proportion of clinical relevant, useful citations (23.5%) and much less confabulated references (27.0%). However, there was still a substantial portion of irrelevant references (22.9%), general textbooks (16.2%), and instances where ChatGPT failed to even provide references (9.0%).

Conclusion
ChatGPT can provide clinically useful references, but significant supervision and validation is required for the tendencies of ChatGPT to fabricate material. Future natural language processing models designed to augment diagnostic medicine may improve dermatopathologists' ability to find clinically useful references.

Evaluating the diagnostic utility of ChatGPT for providing dermatopathology immunohistochemistry reference recommendations

Background

Tumor-to-tumor metastasis (TTM) is an exceptionally rare phenomenon, especially so when involving cutaneous neoplasms. Therefore, they may present a diagnostic conundrum for pathologists.

TTM are defined by the following criteria:

1- More than one primary tumor must exist.

2- The host tumor is a true neoplasm.

3- The primary tumor shows growth inside the secondary tumor, not just transitional growth indicative of collision.

4- The host tumor cannot be a lymph node affected by leukemia or lymphoma.

This systematic review aims to characterize the cutaneous TTM as both donor and recipient tumors. This will provide information for pathologists to consider in the diagnosis of TTM. Additionally, these events may provide insight into underlying mechanisms of tumor tropism in metastatic disease.

Methods

PubMed, Embase, Medline, and ScienceDirect were searched using the terms “tumor-to-tumor metastasis” and “cutaneous””. Titles and abstracts were reviewed independently by 2 reviewers (NC, MRM). Inclusion criteria required English language and peer-reviewed studies. Consensus was reached for all articles. Furthermore, citations of all included studies were

reviewed to find further cases. The following data were extracted: title, patient age, sex, race, donor and recipient tumor type, size, location, subtype, and special features, treatment history, and metastatic burden.

Results

13/673 articles met inclusion criteria and were further reviewed. The most common donor cutaneous malignancy was melanoma (7/13), The most common recipient tumor of cutaneous metastatic melanoma was basal cell carcinoma (2/13). Lipomas and basal cell carcinomas(BCCs) were the two most common recipient cutaneous tumors (2/13 each), and the phenomena often occurred in the setting of widespread metastasis (7/13 cases).

Conclusion

While uncommon, tumor-to-tumor metastasis should be considered as an important differential diagnosis that may have profound implications for treatment and prognosis. Melanomas were implicated in cutaneous TTM more often than any other tumor. This information will be useful for pathologists to consider in working up challenging cases suggestive of tumor-to-tumor metastasis.

Characteristics of TTM identified in this study may elucidate mechanisms underlying metastatic tropism. It is unclear to what degree TTM occurs from chance, or how the host tumor microenvironmental milieu affects the probability of metastasis. Our findings suggest host tumor microenvironment plays an important role in TTM, as only 7/13 of cases occurred in patients with diffuse metastatic burdens. Additional studies teasing apart the seed-and-soil hypothesis of TTM are needed to understand this phenomenon use it to improve diagnosis, prognosis, and treatment of patients with metastatic disease.

Cutaneous Neoplasms as Donors and Recipients in Tumor-to-Tumor Metastasis: A Systematic Review of the Literature

Advancing Cancer Diagnostics with Artificial Intelligence (AI): A Comprehensive Systematic Review

Elevated Infection Risk in Post-Transplant 
Lymphoproliferative Disorder: the Role for Prophylaxis


Background
For patients undergoing radical prostatectomy for prostate cancer (PCa), accurate risk stratification is essential to guide post-prostatectomy decision making. Recently, there has been success in using multi-modal artificial intelligence (AI) to aid in risk stratification. Herein, we present a TRansfer learning-based multi-modal Artificial InteLligence (TRAIL) model for post-prostatectomy biochemical recurrence (BCR) risk stratification using pre-prostatectomy data only.

Methods
Patients contained within a prospective localized PCa registry at a single institution were utilized. Digital pathology slides from the diagnostic biopsies prior to radical prostatectomy were scanned at 20x resolution. Features were extracted from pathology slides via 2 transfer learning steps: (1) Inception-v3 network trained on the PCa PANDA dataset to generate a heatmap corresponding to the tumor areas followed by (2) AlexNet network that learns 256 AI representative features from each slide. Afterwards, TRAIL combines AI + clinical features using random under-sampling and the standard boosting procedure AdaBoost (RUSBoost), and train a classification ensemble model based on weak tree learners to predicts 2yr-BCR. TRAIL training was performed on 245 patients and was then locked and applied to the test set of 119 patients. Accuracy and the area under the curve (AUC) were calculated and compared to CAPRA-S.
After training, the occlusion sensitivity algorithm was used to localize the areas within the slide driving the AI features. Lastly, the visual features areas were segmented using K-mean clustering with 4 classes (background, basal cells, acinar cells, and fibromuscular tissue), and interpretable qualitative features were extracted. 

Results
A total of 808 digital whole pathology biopsy slides from 364 patients treated with radical prostatectomy were included. The median follow-up was 48 months (Range: 1-132 months). The cumulative incidence of 2-and 5-year BCR were 11% and 18% respectively. Clinical factors included age, PSA, and fraction of positive biopsy cores. On the testing set, TRAIL achieved a 2-year BCR AUC of 0.81 and accuracy of 0.95, and was superior to CAPRA-S (AUC = 0.57). 
Qualitative studies revealed nests and cords of cells with vague lamina formation and scattered clear vacuoles. Further histopathological review is undergoing.

Conclusion
Through a combination of deep and ensemble learning, TRAIL incorporates clinical and histopathology features, enabling an improved BCR risk stratification post-prostatectomy. In contrast to commonly used biomarkers, TRAIL is independent of prostatectomy data, and offers patients the advantage of decision-forming about prostatectomy prior to undergoing surgery. Future work with larger datasets is warranted to further optimize TRAIL for clinical use.

TRAIL: A Diagnostic Biopsy-derived, Artificial Intelligence-based, End-to-end, Automated, Interpretable, and Predictive Biomarker for Biochemical Recurrence after Prostatectomy

Background
Prostate cancer (PCa) risk is understood to be mostly unmodifiable and inherited, but there is evidence that environmental and behavioral factors may also contribute. A recent study of health professional cohorts suggests a healthy lifestyle can mitigate a high inherited risk of lethal PCa. It is unknown how modifiable factors affect PCa risk in more diverse populations. The objective of this study was to determine the effects of healthy lifestyle on PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population.

Methods
The Million Veteran Program (MVP) is a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. A healthy lifestyle score (range 0-3) was calculated with a point assigned for each of the following at MVP enrollment: not a current smoker, body mass index (BMI) ≤30 and strenuous activity ≥2 days per week. Genetic risk was assessed via a previously validated polygenic hazard score using genotype data from 290 variants. Age at diagnosis of PCa, diagnosis of metastatic PCa, and death from PCa were assessed via Cox proportional hazards models.

Results
Healthy lifestyle score (HLS) was associated with lower risk of metastatic PCa (HR per unit/point of HLS: 0.77, 95% CI 0.73–0.81, p<0.001) and of fatal PCa (HR 0.73, 95% CI 0.67–0.81, p<0.001) in univariable analysis. Black participants with higher HLS similarly had reduced risk of metastatic and fatal PCa. Analysis of HLS components showed smoking to have a large effect on PCa risk: non-smokers were less likely to be diagnosed with PCa (OR 0.89, 95% CI 0.86–0.92, p<0.001) and far less likely to develop metastatic PCa (OR 0.50, 95% CI 0.46–0.55, p<0.001) or fatal PCa (OR 0.33, 95% CI 0.28–0.39, p<0.001). When accounting for race/ethnicity and family history in multivariable analysis, HLS was independently associated with reduced metastatic (HR 0.82, 95% CI 0.77–0.87, p<0.001) and fatal PCa (HR 0.76, 95% CI 0.68–0.86, p<0.01) but not PCa diagnosis (HR 1.01, 95% CI 0.99–1.03). When accounting for genetic risk (PHS290) as well as race/ethnicity and family history, HLS remained independently associated with reduced metastatic and fatal PCa. 

Conclusion
Adherence to a healthy lifestyle is associated with reduced risk of metastatic or fatal PCa, which offsets inherited risk.

Premium content

Next from AMA Research Challenge 2023

Association of plasma NRP2 and VEGF-C levels with prostate cancer disease severity

Similar lecture

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting