Correlation between FISH status and MYC immunohistochemistry results.

<p>Correlation between FISH status and MYC immunohistochemistry results.</p

Correlation between MYC immunohistochemistry and <i>MYC</i> rearrangement.

<p><i>p</i>-value <0.00010.</p

Overall survival of patients with DLBCL based on alterations in MYC, BCL2 and FOXP1.

<p>Kaplan Meier curves represents OS according to A) MYC protein expression, B) FOXP1 protein expression, C) presence of MYC and BCL2 expression, D) <i>MYC</i> translocation, E) <i>BCL2</i> translocation, F) BCL2 protein expression.</p

Univariate analysis of the biologic factors predictive of survival in DLBCL.

<p>Univariate analysis of the biologic factors predictive of survival in DLBCL.</p

Multivariate logistic regression analysis: <i>E*01</i>:<i>01</i> contributes independently of other <i>HLA</i> factors to the risk of EBV+ cHL.

<p>OR: odds ratio; CI, confidence interval</p><p>Multivariate logistic regression analysis: <i>E*01</i>:<i>01</i> contributes independently of other <i>HLA</i> factors to the risk of EBV+ cHL.</p

Patient inclusion/exclusion criteria, reproduced from [34].

Patient inclusion/exclusion criteria, reproduced from [34].</p

Appendix A: Think aloud protocol.

Machine learning tools are increasingly used to improve the quality of care and the soundness of a treatment plan. Explainable AI (XAI) helps users in understanding the inner mechanisms of opaque machine learning models and is a driver of trust and adoption. Explanation methods for black-box models exist, but there is a lack of user studies on the interpretability of the provided explanations. We used a Think Aloud Protocol (TAP) to explore oncologists’ assessment of a lung cancer relapse prediction system with the aim of refining the purpose-built explanation model for better credibility and utility. Novel to this context, TAP is used as a neutral methodology to elicit experts’ thought processes and judgements of the AI system, without explicit prompts. TAP aims to elicit the factors which influenced clinicians’ perception of credibility and usefulness of the system. Ten oncologists took part in the study. We conducted a thematic analysis of their verbalized responses, generating five themes that help us to understand the context within which oncologists’ may (or may not) integrate an explainable AI system into their working day.</div

Analysis of the combined effect of <i>HLA-E*01</i>:<i>01</i>, <i>A*01</i> and <i>A*02</i> on the risk of EBV-associated cHL.

<p>OR: odds ratio; CI, confidence interval</p><p>^+ve: positive</p><p>^-ve: negative</p><p>Analysis of the combined effect of <i>HLA-E*01</i>:<i>01</i>, <i>A*01</i> and <i>A*02</i> on the risk of EBV-associated cHL.</p

Analysis of the <i>HLA-E*01</i>:<i>01 allele</i> as a protective factor in cHL stratified by EBV status.

<p>HPH: patients from Hospital Puerta de Hierro, MDA: patients from MD Anderson Cancer Center</p><p>OR: odds ratio; CI, confidence interval</p><p>^a Genotypes with the protective allele (homozygous <i>E*01</i>:<i>01</i> and heterozygous)</p><p>Analysis of the <i>HLA-E*01</i>:<i>01 allele</i> as a protective factor in cHL stratified by EBV status.</p

Characteristics of the cHL patients and control subjects.

<p>HPH, patients from the Hospital Puerta de Hierro. MDA, patients from MD Anderson Cancer Center. NC: not classified.</p><p>Characteristics of the cHL patients and control subjects.</p