Variation in prostate cancer growth rates in an MRI-based active surveillance cohort.

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Title: Variation in prostate cancer growth rates in an MRI-based active surveillance cohort.
Authors: Smith, Hayley1 (AUTHOR) hs898@cam.ac.uk, Stavrinides, Vasilis2,3,4 (AUTHOR), Giganti, Francesco5,6 (AUTHOR), Moore, Caroline M.3,5 (AUTHOR), Narayanan, Bharath1 (AUTHOR), Emberton, Mark5,7 (AUTHOR), Pharoah, Paul D. P.8 (AUTHOR), Pashayan, Nora1 (AUTHOR)
Source: European Radiology. Jun2026, Vol. 36 Issue 6, p5094-5105. 12p.
Subjects: Tumor growth, Gompertz functions (Mathematics), Prostate biopsy, Prostate cancer, Magnetic resonance imaging, Prostate-specific antigen, Watchful waiting
Abstract: Background: Understanding tumour growth rates helps optimise screening and active surveillance (AS) schedules. We estimated prostate cancer growth rate accounting for individual variation in a longitudinal AS cohort. Materials and methods: We modelled tumour growth in 145 biopsy-confirmed prostate cancer patients undergoing MRI-based AS. Primary lesion volumes were measured longitudinally using planimetry. We compared three mixed-effects models (exponential, Gompertz, and logistic) and investigated relationships between growth rate and clinical characteristics. We estimated the natural trajectory of prostate cancer lesions starting at a single cell to clinical detectability (diameter ≈ 1 cm), with diameters estimated based on spherical volume. Results: All three models fit observed data well; however, only the Gompertz model provided reasonable estimates from a single cell to an MRI-detectable size (diameter ≈ 3 mm). The Gompertz growth parameter (mean = 0.07, range = 0.02–0.15), describing exponential growth deceleration, was positively correlated with: patient age; lesion volume at AS onset; prostate-specific antigen (PSA) level; and PSA density. Lesions with Gleason 3 + 4 had faster volume doubling times than Gleason 3 + 3 lesions (mean = 3.5 and 5.2 years, respectively). On average, it would take 17 years (95% CI [15, 19]) for a lesion to grow from a single cell to an MRI-detectable size and an additional 12 years to reach a clinically detectable size (95% CI [10, 13]). At age 50, 75% of lesions would remain undetectable by MRI. Conclusions: Prostate cancer shows slow growth with large variation between patients, posing a challenge for early detection. Key Points: QuestionWhat is the population distribution of growth rates and the natural history of prostate cancer? FindingsProstate cancer typically grows slowly, with considerable variation between individuals. On average, lesions take 17 years to grow from initiation to an MRI-detectable size. Clinical relevanceSmall lesions undetectable on MRI may take many years to reach a clinically significant size, posing a challenge for early detection, as it increases the risk of detecting indolent lesions that may never cause harm. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Variation in prostate cancer growth rates in an MRI-based active surveillance cohort.
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  Data: <searchLink fieldCode="AR" term="%22Smith%2C+Hayley%22">Smith, Hayley</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hs898@cam.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Stavrinides%2C+Vasilis%22">Stavrinides, Vasilis</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Giganti%2C+Francesco%22">Giganti, Francesco</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moore%2C+Caroline+M%2E%22">Moore, Caroline M.</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Narayanan%2C+Bharath%22">Narayanan, Bharath</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Emberton%2C+Mark%22">Emberton, Mark</searchLink><relatesTo>5,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pharoah%2C+Paul+D%2E+P%2E%22">Pharoah, Paul D. P.</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pashayan%2C+Nora%22">Pashayan, Nora</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Jun2026, Vol. 36 Issue 6, p5094-5105. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Tumor+growth%22">Tumor growth</searchLink><br /><searchLink fieldCode="DE" term="%22Gompertz+functions+%28Mathematics%29%22">Gompertz functions (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+biopsy%22">Prostate biopsy</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+cancer%22">Prostate cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate-specific+antigen%22">Prostate-specific antigen</searchLink><br /><searchLink fieldCode="DE" term="%22Watchful+waiting%22">Watchful waiting</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Background: Understanding tumour growth rates helps optimise screening and active surveillance (AS) schedules. We estimated prostate cancer growth rate accounting for individual variation in a longitudinal AS cohort. Materials and methods: We modelled tumour growth in 145 biopsy-confirmed prostate cancer patients undergoing MRI-based AS. Primary lesion volumes were measured longitudinally using planimetry. We compared three mixed-effects models (exponential, Gompertz, and logistic) and investigated relationships between growth rate and clinical characteristics. We estimated the natural trajectory of prostate cancer lesions starting at a single cell to clinical detectability (diameter ≈ 1 cm), with diameters estimated based on spherical volume. Results: All three models fit observed data well; however, only the Gompertz model provided reasonable estimates from a single cell to an MRI-detectable size (diameter ≈ 3 mm). The Gompertz growth parameter (mean = 0.07, range = 0.02–0.15), describing exponential growth deceleration, was positively correlated with: patient age; lesion volume at AS onset; prostate-specific antigen (PSA) level; and PSA density. Lesions with Gleason 3 + 4 had faster volume doubling times than Gleason 3 + 3 lesions (mean = 3.5 and 5.2 years, respectively). On average, it would take 17 years (95% CI [15, 19]) for a lesion to grow from a single cell to an MRI-detectable size and an additional 12 years to reach a clinically detectable size (95% CI [10, 13]). At age 50, 75% of lesions would remain undetectable by MRI. Conclusions: Prostate cancer shows slow growth with large variation between patients, posing a challenge for early detection. Key Points: QuestionWhat is the population distribution of growth rates and the natural history of prostate cancer? FindingsProstate cancer typically grows slowly, with considerable variation between individuals. On average, lesions take 17 years to grow from initiation to an MRI-detectable size. Clinical relevanceSmall lesions undetectable on MRI may take many years to reach a clinically significant size, posing a challenge for early detection, as it increases the risk of detecting indolent lesions that may never cause harm. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-025-12248-y
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        Text: English
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        Type: general
      – SubjectFull: Gompertz functions (Mathematics)
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              Text: Jun2026
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              Y: 2026
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