Development of metastatic brain disease involves progression through lung metastases in EGFR mutated non-small cell lung cancer

Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models

OBJECTIVE

Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable.

METHODS

To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume.

RESULTS

Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature.

CONCLUSIONS

In summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

Prediction of Metastatic Patterns in Bladder Cancer: Spatiotemporal Progression and Development of a Novel, Web-based Platform for Clinical Utility

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 patterns of BCa metastasis and sites of spread indicated underlying organotropic mechanisms in the prediction of response. This recognition opens the possibility of organ site-specific therapeutic targeting in the oligometastatic BCa setting. In the precision medicine era, visualization of complex, time-resolved clinical data will enhance management of postoperative metastatic BCa patients.

Patient summary

We developed a web-based calculator to forecast metastatic progression for individual bladder cancer (BCa) patients, based on the clinical and demographic information obtained at diagnosis. This can help in predicting disease status and survival, and improving management in postoperative metastatic BCa patients.

Take  Home Message

Future pathways of metastatic progression for individual bladder cancer patients can be determined based on currently available clinical and demographic information obtained at diagnosis. In focused subgroups of patients, these metastatic spread patterns can also portend disease status and survival.

Systems Biology of Cancer Metastasis

Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.