Philips forms new cardio-oncology collaboration

AuntMinnieEurope.com staff writers

Jan 31, 2025

Philips has formed a new cardio-oncology partnership with U.S. cardiac MRI software developer, Myocardial Solutions.

The collaboration aims to improve survival rates and bridge a significant gap between heart and cancer care, Philips said. A combination of the MRI acquisition sequence Fast-SENC, the MyoStrain analysis tool, and Philips' SmartSpeed and BlueSeal technologies will be used to identify heart disease due to cardiotoxicity in cancer survivors and patients undergoing cancer therapies.

Philips said the technologies can detect heart failure across 48 segments of the heart in 10 minutes, with less than five minutes of analysis time to help identify regional dysfunction before it impacts the entire heart.

German researcher Prof. Dr. Sebastian Kelle from the German Heart Center and Charité University Medicine in Berlin will present his experience using the technologies during the Society for Cardiovascular Magnetic Resonance Annual Scientific Congress in Washington, DC.

C-index for Cox regression and random survival forests models using two feature sets. Adding historical registry data decreases the C-index for Cox regression but increased it for the random survival forests model, consistent with the non-linear structure in the registry data.

AI gives a boost to cardiac MRI risk prediction

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Biparametric prostate MRI identified clinically significant Gleason 3+4 prostate cancer using T2-weighted imaging, diffusion-weighted imaging, and ADC mapping alone.

SERAM 2026: Biparametric MRI cuts time and costs in prostate cancer imaging

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Preablation, postablation, and recurrence multiparametric MRI scans depict a 63-year-old man treated with transperineal focal laser ablation (TPLA) for localized prostate cancer. Clinical parameters included an initial prostate-specific antigen (PSA) level of 7.2 ng/mL, a Prostate Imaging Reporting and Data System (PI-RADS) grade 4 lesion (red circles) in the right apical peripheral zone, and a maximum tumor length of 5 mm. MRI-guided targeted biopsy confirmed a Gleason score 3+4 prostate cancer tumor. (A) Pre-ablation axial T2-weighted MRI scan and apparent diffusion coefficient (ADC) image shows the apical PI-RADS 4 lesion (circles). (B) Six-month post-ablation axial T2-weighted MRI scan and ADC image show cystic necrosis (arrows). There was no evidence of recurrence; the PSA level was 1.5 ng/mL. (C) One-year after ablation, the patient’s PSA level had increased to 4.3 ng/mL. Axial T2-weighted MRI scan and the ADC image shows a recurrent lesion (blue circles) in the right apical peripheral zone adjacent to the ablation scar. Targeted biopsy confirmed Gleason score 3+4 prostate cancer, which was classified as in-field recurrence of clinically significant prostate cancer and subsequently re-treated with TPLA. In summary, serial multiplanar MRI demonstrates treatment response after TPLA with post-ablation necrosis, followed by MRI-detected IFR confirmed with biopsy at 12-month follow-up. Multiparametric MRI was performed with a 3.0-T MRI scanner (Skyra; Siemens Healthineers) using T2-weighted, diffusion-weighted, and ADC sequences, with gadobenate dimeglumine contrast material administration (Bracco Diagnostics).

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C-index for Cox regression and random survival forests models using two feature sets. Adding historical registry data decreases the C-index for Cox regression but increased it for the random survival forests model, consistent with the non-linear structure in the registry data.

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Biparametric prostate MRI identified clinically significant Gleason 3+4 prostate cancer using T2-weighted imaging, diffusion-weighted imaging, and ADC mapping alone.

Image-guided focal laser ablation effective for prostate cancer

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MRI-directed, ultrasound-guided TPLA may be a promising technique to treat prostate cancer while avoiding radical therapies.

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Preablation, postablation, and recurrence multiparametric MRI scans depict a 63-year-old man treated with transperineal focal laser ablation (TPLA) for localized prostate cancer. Clinical parameters included an initial prostate-specific antigen (PSA) level of 7.2 ng/mL, a Prostate Imaging Reporting and Data System (PI-RADS) grade 4 lesion (red circles) in the right apical peripheral zone, and a maximum tumor length of 5 mm. MRI-guided targeted biopsy confirmed a Gleason score 3+4 prostate cancer tumor. (A) Pre-ablation axial T2-weighted MRI scan and apparent diffusion coefficient (ADC) image shows the apical PI-RADS 4 lesion (circles). (B) Six-month post-ablation axial T2-weighted MRI scan and ADC image show cystic necrosis (arrows). There was no evidence of recurrence; the PSA level was 1.5 ng/mL. (C) One-year after ablation, the patient’s PSA level had increased to 4.3 ng/mL. Axial T2-weighted MRI scan and the ADC image shows a recurrent lesion (blue circles) in the right apical peripheral zone adjacent to the ablation scar. Targeted biopsy confirmed Gleason score 3+4 prostate cancer, which was classified as in-field recurrence of clinically significant prostate cancer and subsequently re-treated with TPLA. In summary, serial multiplanar MRI demonstrates treatment response after TPLA with post-ablation necrosis, followed by MRI-detected IFR confirmed with biopsy at 12-month follow-up. Multiparametric MRI was performed with a 3.0-T MRI scanner (Skyra; Siemens Healthineers) using T2-weighted, diffusion-weighted, and ADC sequences, with gadobenate dimeglumine contrast material administration (Bracco Diagnostics).

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Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).

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