Publications by authors named "J McMichael"
Background: Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance.
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- People diagnosed with cancer and their caregivers are overwhelmed by a massive amount of complex information due to advancements in cancer diagnostics and treatments.
- The commentary highlights both the opportunities to improve cancer care and the challenges posed by managing and understanding this large volume of data.
- It emphasizes the importance of integrating this information effectively into everyday cancer care to benefit patients and their support systems.
Article Synopsis
- Gastric cancer (GC) is a serious cancer that affects many people worldwide, but it's not very common in the U.S., making it hard to screen everyone for it.
- Researchers created a computer program using health records to find people who are at higher risk for a specific type of gastric cancer called noncardia gastric cancer (NCGC).
- The study showed that the program worked well by correctly identifying high-risk individuals, and they plan to keep improving it so more people can get screened if needed.
Background: Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance.
View Article and Find Full Text PDFObjective: Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
Methods: Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified.