Background
Bladder cancer (BCa), the sixth commonest cancer in the USA, is highly lethal when metastatic. Spatial and temporal patterns of patient-specific metastatic spread are deemed random and unpredictable. Whether BCa metastatic patterns can be quantified and predicted more accurately is unknown.
Objective
To develop a web-based calculator for forecasting metastatic progression in individual BCa patients.
Design, setting, and participants
We used a prospectively collected longitudinal dataset of 3503 BCa patients who underwent a radical cystectomy following diagnosis and were enrolled continuously. We subdivided patients by their pathologic subgroup stages of organ confined (OC), extravesical (EV), and node positive (N+). We illustrated metastatic pathway progression using color-coded, circular, tree ring diagrams. We created a dynamical, data-visualization, web-based platform that displays temporal, spatial, and Markov modeling figures with predictive capability.
Outcome measurements and statistical analysis
Patients underwent history and physical examination, serum studies, and liver function tests. Surveillance follow-up included computed tomography scans, chest x-rays, and radiographic evaluation of the reservoir and upper tracts, with bone scans performed only if clinically indicated. Outcomes were measured by time to clinical recurrence and overall or progression-free survival.
Results and limitations
Metastases developed in 29% of patients (
n
= 812; median follow-up 15.3 yr), with 5-yr overall survival of 20.2%, compared with 78.6% in those without metastases (
n
= 1983; median follow-up 10.9 yr). The three commonest sites of spread at the time of first progression were bone (
n
= 214; 26.4%), pelvis (
n
= 194; 23.9%), and lung (
n
= 194; 23.9%). The order and frequency of these sites vary when divided by pathologic subgroup stages of OC (lung [
n
= 65; 25.1%], urethra [
n
= 45; 17.4%], and bone [
n
= 29; 11.2%]), EV (pelvis [
n
= 63; 33.0%], bone [
n
= 45; 23.6%], and lung [
n
= 29; 15.2%]), and N+ (bone [
n
= 111; 30.7%], retroperitoneum [
n
= 70; 19.3%], and pelvis [
n
= 60; 16.6%]). Markov chain modeling indicated a higher probability of spread from bladder to bone (15.5%), pelvis (14.7%), and lung (14.2%).
Conclusions
Our web-based calculator allows real-time analyses in the clinic based on individual patient-specific demographic and cancer data elements. For contrasting subgroups, the models indicated differences in Markov transition probabilities. Spatiotemporal pa...