Machine intelligence in non-invasive endocrine cancer diagnostics

Thomasian, Nicole M; Kamel, Ihab R.; Bai, Harrison X.

doi:10.1038/s41574-021-00543-9

Cited by 37 publications

(14 citation statements)

References 174 publications

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“…Also, radiological features can predict prognoses, such as survival, tumor metastasis, and treatment response, and correlate with genomic, transcriptomic, or proteomic features [88][89][90][91]. Conventional workflow in radiomics usually contains four steps: image acquisition, segmentation, feature extraction, and analysis [92]. For image acquisition, standard protocols are needed to minimize confounding variables [93].…”

Section: Ai In Medical Images-based Diagnosismentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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Section: Ai In Medical Images-based Diagnosismentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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“…Quantum networks offer possibilities for the secure transfer of genome files; for example, in next-generation federated data sharing for large-scale research using encrypted blockchain-based quantum networks [81]. The benefits of blockchain technology as a secure smart network automation technology, particularly for genomic data sharing, have been proposed [82] and have seen practical deployment for genomic data privacy in wholehuman genome sequencing projects such as Nebula Genomics [83].…”

Section: Quantum Genomicsmentioning

confidence: 99%

Quantum Neurobiology

2022

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Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

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“…Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is crucial to the triage of therapy and interdisciplinary decision-making. In this context of medical prognosis and prediction analysis, combining patient data with statistical methods, algorithms and tools that constitute the field of Machine Learning (ML) entails a distinct impact on medical research and clinical practice (20)(21)(22)(23)(24)(25). As such, we aimed at examining whether and how contemporary ML methods can facilitate outcome prediction of first-line surgery in prolactinoma patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas

et al. 2022

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BackgroundFirst-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making.ObjectiveTo evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia.MethodsBy jointly examining two independent performance metrics – the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) – in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers.ResultsWe demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 – 1.00; MCC: 0.85, 95% CI: 0.60 – 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 – 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 – 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term.ConclusionsThis study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers.

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Machine intelligence in non-invasive endocrine cancer diagnostics

Cited by 37 publications

References 174 publications

Artificial intelligence in pancreatic cancer

Artificial intelligence in pancreatic cancer

Quantum Neurobiology

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas

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