Publications by authors named "H Minn"

189 Publications

Repeatability of hypoxia dose painting by numbers based on EF5-PET in head and neck cancer.

Acta Oncol 2021 Jun 29:1-6. Epub 2021 Jun 29.

Department of Medical Physics, Oslo University Hospital, Oslo, Norway.

Background: Hypoxia dose painting is a radiotherapy technique to increase the dose to hypoxic regions of the tumour. Still, the clinical effect relies on the reproducibility of the hypoxic region shown in the medical image. F-EF5 is a hypoxia tracer for positron emission tomography (PET), and this study investigated the repeatability of F-EF5-based dose painting by numbers (DPBN) in head and neck cancer (HNC).

Materials And Methods: Eight HNC patients undergoing two F-EF5-PET/CT sessions (A and B) before radiotherapy were included. A linear conversion of PET signal intensity to radiotherapy dose prescription was employed and DPBN treatment plans were created using the image basis acquired at each PET/CT session. Also, plan A was recalculated on the image basis for session B. Voxel-by-voxel Pearson's correlation and quality factor were calculated to assess the DPBN plan quality and repeatability.

Results: The mean (SD) correlation coefficient between DPBN prescription and plan was 0.92 (0.02) and 0.93 (0.02) for sessions A and B, respectively, with corresponding quality factors of 0.02 (0.002) and 0.02 (0.003), respectively. The mean correlation between dose prescriptions at day A and B was 0.72 (0.13), and 0.77 (0.12) for the corresponding plans. A mean correlation of 0.80 (0.08) was found between plan A, recalculated on image basis B, and plan B.

Conclusion: Hypoxia DPBN planning based on F-EF5-PET/CT showed high repeatability. This illustrates that F-EF5-PET provides a robust target for dose painting.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/0284186X.2021.1944663DOI Listing
June 2021

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice.

Front Oncol 2021 26;11:583921. Epub 2021 May 26.

Department of Radiology, University of Turku, Turku, Finland.

Purpose: To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.

Methods: Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm, respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential).

Results: Significant changes were observed in DWI data during the tumor growth, indicated by ADC, ADC, and ADC. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1-4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model.

Conclusion: Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2021.583921DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188898PMC
May 2021

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit.

ESMO Open 2021 Jun 3;6(3):100175. Epub 2021 Jun 3.

University of Turku, Turku, Finland; Department of Oncology, Turku University Hospital, Turku, Finland; FICAN West Cancer Centre, Turku, Finland. Electronic address:

Background: Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the 2020 Surgeon General's report, smoking cessation may reduce CM but supporting evidence is not wide. Use of deep learning-based modeling that enables universal natural language processing of medical narratives to acquire population-based real-life smoking data may help overcome the challenge. We assessed the effect of smoking status and within-1-year smoking cessation on CM by an in-house adapted freely available language processing algorithm.

Materials And Methods: This cross-sectional real-world study included 29 823 patients diagnosed with cancer in 2009-2018 in Southwest Finland. The medical narrative, International Classification of Diseases-10th edition codes, histology, cancer treatment records, and death certificates were combined. Over 162 000 sentences describing tobacco smoking behavior were analyzed with ULMFiT and BERT algorithms.

Results: The language model classified the smoking status of 23 031 patients. Recent quitters had reduced CM [hazard ratio (HR) 0.80 (0.74-0.87)] and OM [HR 0.78 (0.72-0.84)] compared to persistent smokers. Compared to never smokers, persistent smokers had increased CM in head and neck, gastro-esophageal, pancreatic, lung, prostate, and breast cancer and Hodgkin's lymphoma, irrespective of age, comorbidities, performance status, or presence of metastatic disease. Increased CM was also observed in smokers with colorectal cancer, men with melanoma or bladder cancer, and lymphoid and myeloid leukemia, but no longer independently of the abovementioned covariates. Specificity and sensitivity were 96%/96%, 98%/68%, and 88%/99% for never, former, and current smokers, respectively, being essentially the same with both models.

Conclusions: Deep learning can be used to classify large amounts of smoking data from the medical narrative with good accuracy. The results highlight the detrimental effects of persistent smoking in oncologic patients and emphasize that smoking cessation should always be an essential element of patient counseling.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.esmoop.2021.100175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182259PMC
June 2021

More Than Meets the Eye: Scientific Rationale behind Molecular Imaging and Therapeutic Targeting of Prostate-Specific Membrane Antigen (PSMA) in Metastatic Prostate Cancer and Beyond.

Cancers (Basel) 2021 May 7;13(9). Epub 2021 May 7.

Institute of Biomedicine, Cancer Research Unit, FICAN West Cancer Center Laboratory, University of Turku and Turku University Hospital, FI-20520 Turku, Finland.

Prostate cancer is the second most common cancer type in men globally. Although the prognosis for localized prostate cancer is good, no curative treatments are available for metastatic disease. Better diagnostic methods could help target therapies and improve the outcome. Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein that is overexpressed on malignant prostate tumor cells and correlates with the aggressiveness of the disease. PSMA is a clinically validated target for positron emission tomography (PET) imaging-based diagnostics in prostate cancer, and during recent years several therapeutics have been developed based on PSMA expression and activity. The expression of PSMA in prostate cancer can be very heterogeneous and some metastases are negative for PSMA. Determinants that dictate clinical responses to PSMA-targeting therapeutics are not well known. Moreover, it is not clear how to manipulate PSMA expression for therapeutic purposes and develop rational treatment combinations. A deeper understanding of the biology behind the use of PSMA would help the development of theranostics with radiolabeled compounds and other PSMA-based therapeutic approaches. Along with PSMA several other targets have also been evaluated or are currently under investigation in preclinical or clinical settings in prostate cancer. Here we critically elaborate the biology and scientific rationale behind the use of PSMA and other targets in the detection and therapeutic targeting of metastatic prostate cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers13092244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125679PMC
May 2021

Kinetic analysis and optimisation of F-rhPSMA-7.3 PET imaging of prostate cancer.

Eur J Nucl Med Mol Imaging 2021 Apr 12. Epub 2021 Apr 12.

Clinical Research Services Turku - CRST Ltd, Turku, Finland.

Purpose: This phase 1 open-label study evaluated the uptake kinetics of a novel theranostic PET radiopharmaceutical, F-rhPSMA-7.3, to optimise its use for imaging of prostate cancer.

Methods: Nine men, three with high-risk localised prostate cancer, three with treatment-naïve hormone-sensitive metastatic disease and three with castration-resistant metastatic disease, underwent dynamic 45-min PET scanning of a target area immediately post-injection of 300 MBq F-rhPSMA-7.3, followed by two whole-body PET/CT scans acquired from 60 and 90 min post-injection. Volumes of interest (VoIs) corresponding to prostate cancer lesions and reference tissues were recorded. Standardised uptake values (SUV) and lesion-to-reference ratios were calculated for 3 time frames: 35-45, 60-88 and 90-118 min. Net influx rates (K) were calculated using Patlak plots.

Results: Altogether, 44 lesions from the target area were identified. Optimal visual lesion detection started 60 min post-injection. The F-rhPSMA-7.3 signal from prostate cancer lesions increased over time, while reference tissue signals remained stable or decreased. The mean (SD) SUV (g/mL) at the 3 time frames were 8.4 (5.6), 10.1 (7) and 10.6 (7.5), respectively, for prostate lesions, 11.2 (4.3), 13 (4.8) and 14 (5.2) for lymph node metastases, and 4.6 (2.6), 5.7 (3.1) and 6.4 (3.5) for bone metastases. The mean (SD) lesion-to-reference ratio increases from the earliest to the 2 later time frames were 40% (10) and 59% (9), respectively, for the prostate, 65% (27) and 125% (47) for metastatic lymph nodes and 25% (19) and 32% (30) for bone lesions. Patlak plots from lesion VoIs signified almost irreversible uptake kinetics. K, SUV and lesion-to-reference ratio estimates showed good agreement.

Conclusion: F-rhPSMA-7.3 uptake in prostate cancer lesions was high. Lesion-to-background ratios increased over time, with optimal visual detection starting from 60 min post-injection. Thus, F-rhPSMA-7.3 emerges as a very promising PET radiopharmaceutical for diagnostic imaging of prostate cancer.

Trial Registration: NCT03995888 (24 June 2019).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00259-021-05346-8DOI Listing
April 2021