Abstract
The combination of clinical knowledge (applicable for a majority of patients, published in the form of review articles), real world evidence (describing more nuanced outcomes for small cohorts) and innovative artificial intelligence algorithms opens potent avenues to re-examine clinical findings and uncover new biomarkers for the prognosis / prediction of therapeutic responses--in a manner that can directly be incorporated in clinical decision support tools.
In this work, neural networks are first developed to reproduce clinical guidelines with >95% accuracy. After mastering the complex knowledge that is generally expected from human doctors, a transfer learning technique was used to sift through de-identified longitudinal data of 9267 patients with multiple myeloma-related conditions at the Vanderbilt University Medical Center using progression free survival (PFS) to quantify therapeutic outcomes. The "precision medicine neural networks" obtained as a result can be compared with conventional and less portable survival model algorithms, using Shapley values to explain prediction differences.
Testing a first hypothesis that "lytic bone lesions are a prognostic factor for poor PFS", a study involving 1530 patients confirmed that the median PFS of 54 ± 6 months (684 censored patients showing progression) in the presence of bone lesions is significantly lower than the 107 ± 40 months (90 censored patients) in the absence of lesions.
Keeping only 179 patients for which a full range of cytogenetic factors are available and using a Cox regression & Random Survival Forests that provide the best fit of the data, we confirmed previous findings for high-risk trisomies 1 and 7, monosomy 13, deletion 12p and translocation t(11;14)(q13;q32). We furthermore uncovered new additional adverse factors del6q, 2+, 21-, 16- to formulate a model that achieves a statistically significant concordance of 0.72 ± 0.07.
Comparing therapeutic effects for patients in a real-world hospital setting with clinical trials, we found that lenalidomide + bortezomib + dexamethasone used in a first-line therapy resulted in lower median PFS of 17-47 months than the 39-52 months published in the SWOG S0777 trial, most likely because comorbidities contributed to shortening the PFS in real-world settings.
We conclude with an analysis of concrete examples where therapeutic recommendations differ from guidelines, explaining the reason with statistically significant cohorts observed in the data.
No relevant conflicts of interest to declare.
