Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

Mitsuyama, Yasuhito; Matsumoto, Toshimasa; Tatekawa, Hiroyuki; Walston, Shannon L; Kimura, Tatsuo; Yamamoto, Akira; Watanabe, Toshio; Miki, Yukio; Ueda, Daiju

doi:10.1016/s2666-7568(23)00133-2

The Lancet Healthy Longevity

2023

DOI: 10.1016/s2666-7568(23)00133-2

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Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

Yasuhito Mitsuyama,

Toshimasa Matsumoto,

Hiroyuki Tatekawa

et al.

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Cited by 10 publications

(1 citation statement)

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“…16,17 In that regard, medical imaging can display phenotypic changes at the organ level that might be useful to measure age-related changes associated with transitions into pathological states. 17,18 Although some research has explored the use of images such as chest radiographies to estimate the age of the patient with the help of deep learning (DL), 19,20 the relationship between aging and prostate MRI has yet to be explored.…”

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confidence: 99%

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Fernandez‐Quilez,

Nordström,

Jäderling

et al. 2023

Magnetic Resonance Imaging

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BackgroundAging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age‐related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored.PurposeTo develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence.Study TypeRetrospective.PopulationFour hundred and sixty‐eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low‐grade PC (Gleason score ≤6) and 131 negative patients, defined as non‐csPC. The model was tested on 90 negative, 52 low‐grade (142 non‐csPC), and 114 csPC patients.Field Strength/Sequence3‐T, axial T2‐weighted spin sequence.AssessmentChronological age was defined as the age of the participant at the time of the visit. Prostate‐specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging‐Reporting and Data System (PI‐RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non‐csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non‐csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI‐RADS ≥ 3 score.Statistical TestsT‐test, Mann–Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant.ResultsAfter adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32–6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non‐csPC than that of adjusted PI‐RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975–0.987).Data ConclusionPAG may be associated with the risk of csPC and could outperform other PC risk factors.Level of Evidence3Technical EfficacyStage 3

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confidence: 99%

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Fernandez‐Quilez,

Nordström,

Jäderling

et al. 2023

Magnetic Resonance Imaging

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Data set terminology of deep learning in medicine: a historical review and recommendation

Walston,

Seki,

Takita

et al. 2024

Jpn J Radiol

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Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

Ueda,

Walston,

Fujita

et al. 2024

Diagnostic and Interventional Imaging

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Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

Cited by 10 publications

References 31 publications

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Data set terminology of deep learning in medicine: a historical review and recommendation

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

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