Pathology fact of the day #pfotd
90% of carcinoid tumors ("NET") of the lung are "typical" and only 10% of these recur. In other words, the prognosis in most carcinoid tumors is very good
Less than 4 weeks to our 3rd annual Cleveland Clinic Soft Tissue Pathology Course May 14-17!!! 150 NEW digital cases in an interactive slide-based format. In person and VIRTUAL option. Lectures available on demand for at least 6 months!!! Register now!!! https://t.co/ZWPev1Ifzs @Williamson_SR@RAlaghehbandan@Janiranavarro@JaneNguyen44@ScottBikeethan@CCFPathRes@CLEClinicLabs #bst #pathtwitter #path #softtissue
It is never too early to begin planning for your next conference, January 2027. This will be a general surgical pathology conference, but I also will make sure we talk about bone/soft tissue pathology (@SteveBillingsMD too!). @CCFPathRes@CLEClinicLabs
https://t.co/L5Ls715pWN
The “illusion of continuity” — assuming your future self wants what you want today — shapes how we lead, teach, and grow in ways we rarely examine.
Shankar Vedantam’s TED talk is a quiet reframe worth 14 minutes.
https://t.co/I2dLWueAgC
majority of tumors in our classification system were described and well documented prior to being also confirmed as having molecular drivers. For sure, molecular testing has helped clarify boundaries, but the classification system has expanded not contracted, with only a
New and #OpenAccess! | Molecular testing and other metrics in thyroid cytology as quality-assurance measures in evaluating variation among pathologists in the diagnosis of atypia of undetermined significance
https://t.co/1KSh4uuPEM
#CytoPath@Raodyisms@erikacytogal@ENT_Path
This review covers all relevant aspects of #telecytology for ROSE, including digital pathology options, operators, validation, quality assurance, reimbursement, and recommendations from professional organizations. 🌹
https://t.co/rE8v0r2AcR
@MDAndersonNews#CytoPath
📊 The Area Under the Curve (AUC) is a single number that shows how well a model can tell categories apart. It combines sensitivity (finding true positives) and specificity (avoiding false positives) into one overall score. A perfect model has an AUC of 1.0—no missed diagnoses, no false alarms. The higher the AUC, the more accurate and reliable the model is across all threshold settings. Think of it as the model’s “diagnostic precision” score!
If you decide to seek a second opinion for a life altering diagnosis, I highly recommend that you have your original pathology diagnosis, when applicable, also reviewed. At many institutions this is (and should be) standard practice.