Mining Pre-Surgical Patterns Able to Discriminate Post-Surgical Outcomes in the Oncological Domain

Alexandre, Leonardo; Costa, Rafael S.; Santos, Lúcio Lara; Henriques, Rui

doi:10.1109/jbhi.2021.3064786

Cited by 10 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biclustering has been largely employed for the effective discovery of gene expression patterns [44][45][46], with pattern-based biclustering showing relevant performance indicators in diverse biological data contexts [47,48]. Biclustering based on PAttern Mining Software (BicPAMS) [49] integrates dispersed state-of-the-art contributions on pattern-based biclustering, allowing for a high level of parametrization while performing efficient searches with guarantees of optimality, statistical significance and discriminative power [50,51].…”

Section: Bicluster-based Space Transformationmentioning

confidence: 99%

On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data

2023

Self Cite

View full text Add to dashboard Cite

Background Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin’s Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. Methods We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin’s Lymphoma patients, obtained through the NanoString’s nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules. Results Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall). Conclusions Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.

show abstract

Section: Bicluster-based Space Transformationmentioning

confidence: 99%

On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Descriptive approaches can be applied to different ends: (1) discovery of discriminative patterns of postsurgical risk, and temporal patterns of recovery progression; (2) learning generative models able to comprehensively capture postsurgical health-and-care outcomes; (3) discriminant feature analysis; (4) clustering of individuals into risk groups; (5) visual analytics to support the study of correlations; and (6) analysis of outlier individuals, including individuals with comorbidities or unexpected outcomes. One example of mining pre-surgical patterns to discriminate postsurgical outcomes in the oncological context is given in [29]. In contrast with descriptive approaches, predictive approaches produce models that can be readily applicable on new patients to assess their postsurgical risks.…”

Section: Taxonomy Of Postsurgical Risk Analysismentioning

confidence: 99%

Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

Gonçalves

Henriques

Costa

2021

Cancers

Self Cite

View full text Add to dashboard Cite

Postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.

show abstract

“…In this context, patterns help mapping regulatory interactions, forming regulatory networks, that provide a vital information to better understand the evolution of the genes, as well as unique regulatory cascades elicited in response to stimuli, disease progression or drug action [10]. These discriminative properties towards an outcome of interest can either be incorporated in the pattern discovery process [11,12], or assessed after extracting classic informative patterns. In both cases, one or multiple interestingness measures, such as confidence [13], statistical significance [14] (probability of pattern occurrence against expectations) and/or discriminative power views [15,16], are combined into pattern-centric models to aid medical decisions and study regulatory responses to events of interest [12,17].…”

Section: Introductionmentioning

confidence: 99%

“…These discriminative properties towards an outcome of interest can either be incorporated in the pattern discovery process [11,12], or assessed after extracting classic informative patterns. In both cases, one or multiple interestingness measures, such as confidence [13], statistical significance [14] (probability of pattern occurrence against expectations) and/or discriminative power views [15,16], are combined into pattern-centric models to aid medical decisions and study regulatory responses to events of interest [12,17].…”

Section: Introductionmentioning

confidence: 99%

DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes

Alexandre

Costa²,

Henriques³

2022

PLoS ONE

View full text Add to dashboard Cite

Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. Availability: DISA is freely available at https://github.com/JupitersMight/DISA under the MIT license.

show abstract

Mining Pre-Surgical Patterns Able to Discriminate Post-Surgical Outcomes in the Oncological Domain

Cited by 10 publications

References 31 publications

On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data

On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data

Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes

Contact Info

Product

Resources

About