Pre-treatment 18F-choline PET/CT is prognostic for biochemical recurrence, development of bone metastasis, and cancer specific mortality following radical local therapy of high-risk prostate cancer

Kjölhede, Henrik; Almquist, Helén; Lyttkens, Kerstin; Bratt, Ola

doi:10.1186/s41824-018-0034-2

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Studies on the prediction of oncological relapse after surgery based on preoperative variables, including modern imaging techniques, are limited. In 2018, Kj€ olhede et al [27] showed that metastatic disease on pre-treatment choline PET/CT was an independent predictor for developing BCR and skeletal metastases after local treatment. In addition, in a recent study by van Leeuwen et al [13] assessing PSMA PET/CT, miN1 on PSMA PET/CT was found to be highly predictive for biochemical persistence on univariate logistic regression analysis, while several other preoperative and histopathological variables (initial PSA value, clinical T-stage, surgical margin status) were not.…”

Section: Discussionmentioning

confidence: 99%

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Predicting early outcomes in patients with intermediate‐ and high‐risk prostate cancer using prostate‐specific membrane antigen positron emission tomography and magnetic resonance imaging

Meijer¹,

Leeuwen

Donswijk

et al. 2021

BJU International

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To identify predictors of early oncological outcomes in patients who opt for robot-assisted laparoscopic radical prostatectomy (RARP) for localized prostate cancer (PCa), including conventional prognostic variables as well as multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography (PET). Patients and MethodsThis observational study included 493 patients who underwent RARP and extended pelvic lymph node dissection (ePLND) for unfavourable intermediate-or high-risk PCa. Outcome measurement was biochemical progression of disease, defined as any postoperative prostate-specific antigen (PSA) value ≥0.2 ng/mL, or the start of additional treatment. Cox regression analysis was performed to assess predictors for biochemical progression, including initial PSA value, biopsy Grade Group (GG), T-stage on mpMRI, and lymph node status on PSMA PET imaging (miN0 vs miN1). ResultsThe median (interquartile range) total follow-up of all included patients without biochemical progression was 12.6 (7.5-22.7) months. When assessing biochemical progression after surgery, initial PSA value (per doubling; odds ratio [OR] 1.22, 95% confidence interval [CI] 1.07-1.40; P = 0.004), biopsy GG ≥4 vs GG 1-2 (OR 1.83, 95% CI 1.18-2.85; P = 0.007), Tstage on mpMRI (rT3a vs rT2: OR 2.13, 95% CI 1.39-3.27; P = 0.001; ≥rT3b vs rT2: OR 4.78, 95% CI 3.20-7.16; P < 0.001) and miN1 on PSMA PET imaging (OR 2.94, 95% CI 2.02-4.27; P < 0.001) were independent predictors of early biochemical progression of disease. ConclusionInitial PSA value, biopsy GG ≥4, ≥rT3 disease on mpMRI and miN1 disease on PSMA PET were predictors of early biochemical progression after RARP. Identifying these patients with an increased risk of early biochemical progression after surgery may have major implications for patient counselling in radical treatment decisions and on patient selection for modern (neo-)adjuvant and systematic treatments.

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Section: Discussionmentioning

confidence: 99%

“…In 2018, Kjölhede et al. [ 27 ] showed that metastatic disease on pre‐treatment choline PET/CT was an independent predictor for developing BCR and skeletal metastases after local treatment. In addition, in a recent study by van Leeuwen et al.…”

Section: Discussionmentioning

confidence: 99%

Predicting early outcomes in patients with intermediate‐ and high‐risk prostate cancer using prostate‐specific membrane antigen positron emission tomography and magnetic resonance imaging

Meijer¹,

Leeuwen

Donswijk

et al. 2021

BJU International

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“…(2014) at the Odense University Hospital in Denmark between March 2010 and May 2014; 399 patients from a study by Kjölhede et al. (2018) at the Skåne University Hospital in Sweden between February 2008 and November 2011; 50 patients from a study by Mortensen et al. (2019) at the Odense University Hospital in Denmark between January 2013 and April 2014.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

Borrelli

Larsson

Ulén³

et al. 2020

Clin Physio Funct Imaging

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Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter‐reader variability. Artificial intelligence (AI)‐based methods can provide an objective image analysis. We aimed at developing and validating an AI‐based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy‐proven PCa who had undergone 18F‐choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI‐based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI‐based lymph node detections were compared to those of two independent readers. The association with PCa‐specific survival was investigated. Results The AI‐based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p = .045) using Reader A as reference. AI‐based tool and Reader A showed similar performance (90 vs. 87/111; p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI‐based tool, PSA, and curative treatment was significantly associated with PCa‐specific survival. Conclusion This study shows the feasibility of using an AI‐based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.

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“…The PET/CT examinations in this set were performed at another institution and with different cameras than the training/validation set in [16]. The patients had been part of previous studies with the following inclusion criteria: newly diagnosed highrisk prostate cancer patients (defined as PSA above 20 ng/ml, and/or clinical tumour stage T3, and/or Gleason score 8-10) with normal or inconclusive bone scintigraphy [18,19]. The exclusion criteria were hormonal therapy before PET/CT, or PSA !150 ng/ml.…”

Section: Patientsmentioning

confidence: 99%

“…PET/CT scans were acquired by means of an integrated PET/ CT system (Philips Gemini TF, Philips Medical Systems, Cleveland, OH, USA) at the Centre for Medical Imaging and Physiology, Skåne University Hospital in Lund or Malm€ o [18,19].…”

Section: Test Setmentioning

confidence: 99%

Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients

Polymeri

Kjölhede

Enqvist

et al. 2021

Scandinavian Journal of Urology

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Objective: Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUV max , representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the diseases activity with prognostic significance, compared with conventional measurements. Methods: An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using 18 F-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUV max were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model. Results: Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p ¼ 0.008), tumour fraction of the gland (p ¼ 0.005), total lesion uptake of the prostate (p ¼ 0.02), and age (p ¼ 0.01) were significantly associated with disease-specific survival, whereas SUV max (p ¼ 0.2), PSA (p ¼ 0.2), and Gleason score (p ¼ 0.8) were not. Conclusion: AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUV max and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.

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Pre-treatment 18F-choline PET/CT is prognostic for biochemical recurrence, development of bone metastasis, and cancer specific mortality following radical local therapy of high-risk prostate cancer

Cited by 5 publications

References 21 publications

Predicting early outcomes in patients with intermediate‐ and high‐risk prostate cancer using prostate‐specific membrane antigen positron emission tomography and magnetic resonance imaging

Predicting early outcomes in patients with intermediate‐ and high‐risk prostate cancer using prostate‐specific membrane antigen positron emission tomography and magnetic resonance imaging

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients

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