Sep
30
Tue
Cancer Educ Seminar: Simulation Modeling of Lung and Breast Cancer Outcomes @ Stanford Cancer Center CC 2103-2104
Sep 30 @ 8:00 am – 9:00 am
Cancer Educ Seminar: Simulation Modeling of Lung and Breast Cancer Outcomes @ Stanford Cancer Center CC 2103-2104

Presenter: Sylvia Plevritis, PhD, Professor of Radiology (General Radiology)

Nov
17
Tue
Cancer Education Seminar: Immunotherapy in Lung Cancer and Forum: Right to Die Legislation @ Cancer Center, CC 2103-05
Nov 17 @ 8:00 am – 9:00 am
Cancer Education Seminar: Immunotherapy in Lung Cancer and Forum: Right to Die Legislation @ Cancer Center, CC 2103-05 | Palo Alto | California | United States

Presenters: Suki Padda, MD, Instructor of Medicine (Oncology): “Immunotherapy in Lung Cancer.” Kavitha Ramchandran, MD, Clinical Assistant Professor of Medicine (Oncology): “Forum Discussion of Right to Die Legislation”.

Nov
24
Tue
Cancer Education Seminar: Survivorship and Surveillance for NSCLC @ Cancer Center, CC 2103-05
Nov 24 @ 8:00 am – 9:00 am
Cancer Education Seminar: Survivorship and Surveillance for NSCLC @ Cancer Center, CC 2103-05 | Palo Alto | California | United States

Presenter: Leah Backhus, MD, Associate Professor of Cardiothoracic Surgery (Thoracic Surgery)

Nov
29
Tue
ID Lecture Series – “Skin/Soft Tissue Infections” @ LK209
Nov 29 @ 8:00 am – 9:00 am

Jose G. Montoya

Presenter: Jose G. Montoya, MD. Professor of Medicine (Infectious Diseases and Geographic Medicine) and Infectious Disease Doctor.

Dec
6
Tue
ID Lecture Series – “Febrile Neutropenia” @ LK209
Dec 6 @ 8:00 am – 9:00 am

Jose G. Montoya

Presenter: Jose G. Montoya, MD. Professor of Medicine (Infectious Diseases and Geographic Medicine) and Infectious Disease Doctor.

Jan
19
Thu
BMIR Research in Progress: Edward H. Lee “A Deep Learning Framework to Predict Survival from Medical Images of Lung Cancer Patients” @ MSOB, Conference Room X-275
Jan 19 @ 12:00 pm – 1:00 pm

Edward H. Lee

Edward H. Lee
Graduate Student
Gevaert Lab, Stanford University

We present a deep learning framework to predict survival of lung cancer patients by using convolutional networks to learn high-dimensional representations of tumor phenotypes from CT images and clinical parameters. We evaluate our framework on three cohorts (626 patients) with survival data using AUC and CI on the model’s predictions, and log-rank and Wilcoxon tests on predicted Kaplan-Meier survival curves. Furthermore, we describe the design of the optimization loss function and show how the injection of training noise can improve the model robustness. We also introduce the concept of priming which improves generalization on completely unseen cohorts. Finally, we briefly describe ways of visualizing learned features.

Apr
27
Thu
BMIR Research in Progress: Mu Zhou“Radiogenomics: Linking Molecular Data and Clinical Imaging Towards Non-invasive Precision Oncology” @ MSOB
Apr 27 @ 12:00 pm – 1:00 pm

Mu Zhou__edited-3

 

Mu Zhou
Postdoctoral Scholar
BMIR, Stanford University

ABSTRACT:

Growing amounts of molecular data and clinical imaging arrays offer opportunities to gain insights into translational cancer research. Exploring the interplay between these heterogeneous cancer data would deepen our understanding for identifying imaging biomarkers of cancer. In this seminar, I will present two projects in the context of lung cancer research to highlight advances of radiogenomics, an emerging area that is defined to find underlying image-to-genomic associations in oncology. First, I will present our work on integrating transcriptome and CT image data in non-small cell lung cancer (NSCLC). We developed a radiogenomics map linking CT-based semantic features with gene expression clusters. More specifically, we proposed a radiogenomics strategy and identified non-invasive image biomarkers with prognostic implications by leveraging 17 independent, public gene expression cohorts with survival outcomes. Second, we extended to develop and validate preoperative high-dimensional image signatures that were predictive in differentiating EGFR mutations patients with lung adenocarcinoma. These methods allow identification of imaging biomarkers to better characterize molecular profiles, creating novel possibilities of non-invasive lung cancer diagnosis at very early stages. Finally, several challenges and ongoing research directions will be addressed in related areas.

Oct
11
Thu
BMIR Research in Progress: Pritam Mukherjee “Early Detection of Cancer in the NLST Dataset” @ MSOB
Oct 11 @ 5:07 pm – 6:07 pm

Pritam Mukherjee,
Postdoctoral Scholar,
BMIR, Stanford University

ABSTRACT:

The National Lung Screening Trial (NLST) screened high risk individuals, heavy smokers and between the age of 55 and 74 years, for lung cancer, using either low dose CT or X-ray imaging, over three rounds at one-year intervals. Overall, for the low-dose CT cohort, about 95% of the positive screening results were actually false positives. Our aim in this project is to develop a deep learning model that can reduce the false positive rate without sacrificing sensitivity. In this talk, we will discuss the main challenges of using the NLST dataset for our deep learning task, and ways to counter them. We will also describe our deep learning based approaches and models and present some of our recent results.

 

Nov
19
Fri
PHS Trainee Research Colloquium | PEdTalks: PHS Education Talks @ Online Event
Nov 19 @ 10:00 am – 11:00 am
PHS Trainee Research Colloquium | PEdTalks: PHS Education Talks @ Online Event

PHS Trainee Research Colloquium
PEdTalks: PHS Education Talks

Event Information and Registration

The Stanford Center for Population Health Sciences (PHS) Trainee program comprises pre- and postdoctoral research fellows. We aim to train the next generation of population health scientists, scholars, and leaders. Please join us on Friday, 11/19/2021 for our first series of PHS Education Talks (PEdTalks), where we will showcase the research of 5 of our trainees.

  • Kayla Kinsler: Influence of Incentive Amount on Physician Participation
  • Cesar Vargas Nunez: Feeling ill:  the infectious effect of perspective-taking on attitudes toward healthcare access for undocumented immigrants
  • Alice Milivinti: Revisiting the Earned Income Tax Credit and Infant Health
  • Sven van Egmond: Unnecessary care for skin cancer
  • Jackie Ferguson: Virtual Disparities: Identifying differences in how Veterans use VA video healthcare