Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects ...the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement.
A total of 392 consecutive patients undergoing
Tc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared.
Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (
< 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (
was not significant NS). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 95% CI, 0.7-0.87; AUC, 0.81 95% CI, 0.73-0.89) and the SVM (0.82 0.74-0.9) for AC data were the same (
= NS) and were higher than those for the unadjusted VP (0.63 0.53-0.73) (
< 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 95% CI, 0.69-0.89; AUC, 0.8 95% CI, 0.72-0.88) and the SVM (0.79 0.71-0.87) were the same (
= NS) and were higher than those for the unadjusted VP (0.65 0.56-0.75) (
< 0.01).
Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.
Breast cancer is the most common cancer in women and the first cancer concerning mortality. Metastatic breast cancer remains a disease with a poor prognosis and about 30% of women diagnosed with an ...early stage will have a secondary progression. Metastatic breast cancer is an incurable disease despite significant therapeutic advances in both supportive cares and targeted specific therapies. In the management of a metastatic patient, each clinician follows a highly complex and strictly personal decision making process. It is based on a number of objective and subjective parameters which guides therapeutic choice in the most individualized or adapted manner.
The main objective is to integrate massive and heterogeneous data concerning the patient's environment, personal and familial history, clinical and biological data, imaging, histological results (with multi-omics data), and microbiota analysis. These characteristics are multiple and in dynamic interaction overtime. With the help of mathematical units with biological competences and scientific collaborations, our project is to improve the comprehension of treatment response, based on health clinical and molecular heterogeneous big data investigation.
Our project is to prove feasibility of creation of a clinico-biological database prospectively by collecting epidemiological, socio-economic, clinical, biological, pathological, multi-omic data and to identify characteristics related to the overall survival status before treatment and within 15 years after treatment start from a cohort of 300 patients with a metastatic breast cancer treated in the institution.
ClinicalTrials.gov identifier (NCT number): NCT03958136 . Registration 21st of May, 2019; retrospectively registered.
Atherothrombotic events in coronary arteries are most often due to rupture of unstable plaque resulting in myocardial infarction. Radiolabeled molecular imaging tracers directed toward cellular ...targets that are unique to unstable plaque can serve as a powerful tool for identifying high-risk patients and for assessing the potential of new therapeutic approaches. Two commonly available radiopharmaceuticals-
F-FDG and
F-NaF-have been used in clinical research for imaging coronary artery plaque, and ongoing clinical studies are testing whether there is an association between
F-NaF uptake and future atherothrombotic events. Other, less available, tracers that target macrophages, endothelial cells, and apoptotic cells have also been tested in small groups of patients. Adoption of molecular imaging of coronary plaque into clinical practice will depend on overcoming major hurdles, ultimately including evidence that the detection of unstable plaque can change patient management and improve outcomes.
Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic ...lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients’ response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.
Abstract
Background: Each year 5 to 10% of new breast cancers are diagnosed with a metastatic staging. Metastatic breast cancer remains an incurable disease despite significant therapeutic advances ...in both supportive cares and targeted specific therapies. The disease is in most cases characterized by the disruption of systemic homeostasis (coinciding with multiple interactive and dependent parameters). Decision algorithms rely on a number of objective and subjective parameters which allow the therapeutic decision making process to become the most individualized or adapted. Extrinsic objectives parameters are currently based on EBM (evidence-based-medicine). Intrinsic subjective parameters are taken into account in decision-making: parameters that are linked to the oncologist's assumptions, such asthe sensitivity to the theoretical efficacy of treatments and the definition of sensitivity. Currently, the clinician rationalizes these therapeutic indications according to the prediction of the treatment response from the "phenotypic classification". Cancer is a complex disease relying on numerous elements in dynamic, organized and evolving interactions, and analysis of a complex system requires a global approach. The research hypothesis is to evolve from a reductionist, disjunctive, analytical view of the characterization of cell components (genes, transcripts, proteins, etc.) to a global, systemic, conjunctive and organizational vision: distinct datasets are linked and we need to unravel these underlying links. With this project, we want to demonstrate the ability to exploit complex data in healthcare and in particular in cancer management. We chose a specific metastatic breast cancer model. Methods: Our project is to integrate massive and heterogeneous data concerning the patient’s environment, personal and familial history, clinical and biological data, imaging, histological results, multi-omics data, and microbiota analysis. These characteristics are multiple and in dynamic interaction overtime. The main objective is to prove feasibility of creation of a clinico-biological database prospectively by collecting epidemiological, socio-economic, clinical, biological, pathological, multi-omics data and to identify characteristics related to the disease progression before treatment and within 15 years after treatment start from a cohort of 300 patients with a metastatic breast cancer treated in our institution. Results: The EPICURE trial opened in December 2018. Overall recruitment as of July 2021 was 116 patients; 72% had history of adjuvant therapy and 28% had immediately metastatic disease. We created three groups: HR+/Her2- (75% of enrolment); HER2+ (12%); and triple-negative breast cancer (13%). For 89% of patients, we obtained metastatic biopsy during screening and at date 20 metastatic biopsies for recurrence. For all patients, we collected blood sample following the flow chart and microbiota at the screening. Conclusion: EPICURE is an original and longitudinal prospective biocollection of metastatic breast cancer patients. We expect answering specific scientific questions regarding metastatic disease with heterogeneous data, especially by collecting data without a priori value or links each other. Clinical trial information: NCT03958136. Funding: EPICURE is funded by the FEDER European fundings, Astra Zeneca and Lilly
Citation Format: Mathilde Colombié, Pascal Jézéquel, Mathieu Rubeaux, Jean-Sebastien Frenel, Frédéric Bigot, Valérie Seegers, Mario Campone. Feasibility of creation of a clinico-biological database: A prospective longitudinal cohort study of metastatic breast cancer patients (epicuresein) abstract. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-20-01.
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image ...registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org . Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods
We investigated fully automatic coronary artery calcium (CAC) scoring and cardiovascular disease (CVD) risk categorization from CT attenuation correction (CTAC) acquired at rest and stress during ...cardiac PET/CT and compared it with manual annotations in CTAC and with dedicated calcium scoring CT (CSCT).
We included 133 consecutive patients undergoing myocardial perfusion 82Rb PET/CT with the acquisition of low-dose CTAC at rest and stress. Additionally, a dedicated CSCT was performed for all patients. Manual CAC annotations in CTAC and CSCT provided the reference standard. In CTAC, CAC was scored automatically using a previously developed machine learning algorithm. Patients were assigned to a CVD risk category based on their Agatston score (0, 1-10, 11-100, 101-400, >400). Agreement in CVD risk categorization between manual and automatic scoring in CTAC at rest and stress resulted in Cohen’s linearly weighted κ of 0.85 and 0.89, respectively. The agreement between CSCT and CTAC at rest resulted in κ of 0.82 and 0.74, using manual and automatic scoring, respectively. For CTAC at stress, these were 0.79 and 0.70, respectively.
Automatic CAC scoring from CTAC PET/CT may allow routine CVD risk assessment from the CTAC component of PET/CT without any additional radiation dose or scan time.
Purpose of Review
Cardiac positron emission tomography (PET) images often contain errors due to cardiac, respiratory, and patient motion during relatively long image acquisition. Advanced motion ...compensation techniques may improve PET spatial resolution, eliminate potential artifacts, and ultimately improve the research and clinical capabilities of PET.
Recent Findings
Combined cardiac and respiratory gating has only recently been implemented in clinical PET systems. Considering that the gated image bins contain much lower counts than the original PET data, they need to be summed after correcting for motion, forming motion-corrected, high-count image volume. Furthermore, automated image registration techniques can be used to correct for motion between CT attenuation scan and PET acquisition.
Summary
While motion correction methods are not yet widely used in clinical practice, approaches including dual-gated non-rigid motion correction and the incorporation of motion correction information into the reconstruction process have the potential to markedly improve cardiac PET imaging.
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