| 1. |
- Åhsberg, Kristina, et al.
(författare)
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Added value of contrast-enhanced mammography (CEM) in staging of malignant breast lesions - A feasibility study
- 2020
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Ingår i: World Journal of Surgical Oncology. - : Springer Science and Business Media LLC. - 1477-7819. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The aim of this feasibility study was to evaluate the added value of contrast-enhanced mammography (CEM) in preoperative staging of malignant breast lesions, beyond standard assessment with digital mammography and ultrasound, as a base for a future prospective randomized trial. Materials and methods: Forty-seven patients, with confirmed or strongly suspected malignant breast lesions after standard assessment (digital mammography (DM) and ultrasound (US)), scheduled for primary surgery, were invited to undergo CEM as an additional preoperative procedure. The primary endpoint was change in treatment due to CEM findings, defined as mastectomy instead of partial mastectomy or contrariwise, bilateral surgery instead of unilateral or neoadjuvant treatment instead of primary surgery. Accuracy in tumour extent estimation compared to histopathology was evaluated by Bland-Altman statistics. Number of extra biopsies and adverse events were recorded. Results: In 10/47 patients (21%), findings from CEM affected the primary treatment. Agreement with histopathology regarding extent estimation was better for CEM (mean difference - 1.36, SD ± 18.45) in comparison with DM (- 4.18, SD ± 26.20) and US (- 8.36, SD ± 24.30). Additional biopsies were taken from 19 lesions in 13 patients. Nine biopsies showed malignant outcome. No major adverse events occurred. Conclusion: The feasibility of preoperative additional CEM was found to be satisfactory without any serious negative effects. Results imply an added value of CEM in preoperative staging of breast cancer. Further evaluation in larger prospective randomized trials is needed.
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| 2. |
- Strand, Fredrik, et al.
(författare)
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Så kan AI valideras för klinisk implementering
- 2023
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Ingår i: Lakartidningen. - 0023-7205 .- 1652-7518. ; 120
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Tidskriftsartikel (refereegranskat)abstract
- Företag marknadsför över 200 mjukvaror för artificiell intelligens (AI) inom radiologi.Vårdgivare behöver ha möjlighet till opartisk och kvalitetssäkrad utvärdering.Projektet Validering av AI inom bröstradiologi (VAI-B) startades för att möta detta behov, initialt inom mammografiscreening.
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| 3. |
- Bjöersdorff, Mimmi, et al.
(författare)
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Detection of lymph node metastases in patients with prostate cancer: Comparing conventional and digital F-18 -fluorocholine PET-CT using histopathology as a reference
- 2022
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X.
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Tidskriftsartikel (refereegranskat)abstract
- Background: Positron emission tomography-computed tomography (PET-CD with [F-18]-fluorocholine (FCH) is used to detect and stage metastatic lymph nodes in patients with prostate cancer. Improvements to hardware and software have recently been made. We compared the capability of detecting regional lymph node metastases using conventional and digital silicon photomultiplier (SiPM)-based PET-CT technology for FCH. Extended pelvic lymph node dissection (ePLND) histopathology was used as a reference method. Methods: The study retrospectively examined 177 patients with intermediate or high-risk prostate cancer who had undergone staging with FCH PET-CT before ePLND. Images were obtained with either the conventional Philips Gemini PET-CT (n = 93) or the digital SiPM-based GE Discovery MI PET-CT (n = 84) and compared. Results: Images that were obtained using the Philips Gemini PET-CT system showed 19 patients (20%) with suspected lymph node metastases, whereas the GE Discovery MI PET-CT revealed 36 such patients (43%). The sensitivity, specificity, and positive and negative predictive values were 0.3, 0.84, 0.47, and 0.72 for the Philips Gemini, while they were 0.58, 0.62, 0.31, and 0.83 for the GE Discovery MI, respectively. The areas under the curves in a receiver operating characteristic curve analysis were similar between the two PET-CT systems (0.57 for Philips Gemini and 0.58 for GE Discovery MI, p = 0.89). Conclusions: Marked differences in sensitivity and specificity were found for the different PET-CT systems, although the overall diagnostic performance was similar. These differences are probably due to differences in both hardware and software, including reconstruction algorithms, and should be considered when new technology is introduced.
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| 4. |
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| 5. |
- Aaltonen, H. Laura, et al.
(författare)
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Airspace dimension assessment with nanoparticles as a proposed biomarker for emphysema
- 2021
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Ingår i: Thorax. - : BMJ. - 0040-6376 .- 1468-3296. ; 76:10, s. 1040-1043
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Tidskriftsartikel (refereegranskat)abstract
- Airspace dimension assessment with nanoparticles (AiDA) is a novel method to measure distal airspace radius non-invasively. In this study, AiDA radii were measured in 618 individuals from the population-based Swedish CArdiopulmonary BioImaging Study, SCAPIS. Subjects with emphysema detected by computed tomography were compared to non-emphysematous subjects. The 47 individuals with mainly mild-to-moderate visually detected emphysema had significantly larger AiDA radii, compared with non-emphysematous subjects (326±48 μm vs 291±36 μm); OR for emphysema per 10 μm: 1.22 (1.13-1.30, p<0.0001). Emphysema according to CT densitometry was similarly associated with larger radii compared with non-emphysematous CT examinations (316±41 μm vs 291 μm±26 μm); OR per 10 μm: 1.16 (1.08-1.24, p<0.0001). The results are in line with comparable studies. The results show that AiDA is a potential biomarker for emphysema in individuals in the general population.
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| 6. |
- Axelsson, Rebecca, et al.
(författare)
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Computer model of mechanical imaging acquisition for virtual clinical trials
- 2021
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Ingår i: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640191 ; 11595, s. 1-115950
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Konferensbidrag (refereegranskat)abstract
- Malignant breast tumours can be distinguished from benign lesions and normal tissue based on their mechanical properties. Our pilot studies have demonstrated the potential of using Mechanical Imaging (MI) combined with mammography to reduce recalls and false positives in breast cancer screening by more accurately identifying benign lesions. To enable further optimization of MI we propose a computer simulation of the MI acquisition, for use in a Virtual Clinical Trial (VCT) framework. VCTs are computer simulated clinical trials used to efficiently evaluate clinical imaging systems. A linear elastic finite element (FE) model of the breast under dynamic compression was implemented using an open-source FE solver. A spherical tumour (15 mm in diameter) was inserted into the simulated predominantly adipose breast. The location and stiffness of the tumour was varied. The average stress on the compressed breast surface was calculated and compared with the local average stress at the tumour location and the Relative Mean Pressure over lesion Area (RMPA) was calculated. Preliminary results were within a realistic range with an average stress on the breast (tumour) of 5.9-16.6 kPa which is in agreement with published values between 1.0 - 22.5 kPa. This corresponds to RMPA values of 0.96-2.15 depending on stiffness and location of the tumour. This can lead to more detailed validation of various MI acquisition schemes through VCTs before their use in clinical studies.
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| 7. |
- Barufaldi, Bruno, et al.
(författare)
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Virtual Clinical Trials in Medical Imaging System Evaluation and Optimisation
- 2021
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Ingår i: Radiation Protection Dosimetry. - : Oxford University Press (OUP). - 1742-3406 .- 0144-8420. ; 195:3-4, s. 363-371
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Tidskriftsartikel (refereegranskat)abstract
- Virtual clinical trials (VCTs) can be used to evaluate and optimise medical imaging systems. VCTs are based on computer simulations of human anatomy, imaging modalities and image interpretation. OpenVCT is an open-source framework for conducting VCTs of medical imaging, with a particular focus on breast imaging. The aim of this paper was to evaluate the OpenVCT framework in two tasks involving digital breast tomosynthesis (DBT). First, VCTs were used to perform a detailed comparison of virtual and clinical reading studies for the detection of lesions in digital mammography and DBT. Then, the framework was expanded to include mechanical imaging (MI) and was used to optimise the novel combination of simultaneous DBT and MI. The first experiments showed close agreement between the clinical and the virtual study, confirming that VCTs can predict changes in performance of DBT accurately. Work in simultaneous DBT and MI system has demonstrated that the system can be optimised in terms of the DBT image quality. We are currently working to expand the OpenVCT software to simulate MI acquisition more accurately and to include models of tumour growth. Based on our experience to date, we envision a future in which VCTs have an important role in medical imaging, including support for more imaging modalities, use with rare diseases and a role in training and testing artificial intelligence (AI) systems.
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| 8. |
- Bengtsson, Johan, et al.
(författare)
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Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy : Retrospective multicenter study with different MRI scanners
- 2023
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: MRI is an important tool in the prostate cancer work-up, with special emphasis on the ADC sequence. This study aimed to investigate the correlation between ADC and ADC ratio compared to tumor aggressiveness determined by a histopathological examination after radical prostatectomy.Methods: Ninety-eight patients with prostate cancer underwent MRI at five different hospitals prior to radical prostatectomy. Images were retrospectively analyzed individually by two radiologists. The ADC of the index lesion and reference tissues (contralateral normal prostatic, normal peripheral zone, and urine) was recorded. Absolute ADC and different ADC ratios were compared to tumor aggressivity according to the ISUP Gleason Grade Groups extracted from the pathology report using Spearman’s rank correlation coefficient (ρ). ROC curves were used to evaluate the ability to discriminate between ISUP 1-2 and ISUP 3-5 and intra class correlation and Bland-Altman plots for interrater reliability.Results: All patients had prostate cancer classified as ISUP grade ≥ 2. No correlation was found between ADC and ISUP grade. We found no benefit of using the ADC ratio over absolute ADC. The AUC for all metrics was close to 0.5, and no threshold could be extracted for prediction of tumor aggressivity. The interrater reliability was substantial to almost perfect for all variables analyzed.Conclusions: ADC and ADC ratio did not correlate with tumor aggressiveness defined by ISUP grade in this multicenter MRI study. The result of this study is opposite to previous research in the field.
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| 9. |
- Chaudhry, Nadia, et al.
(författare)
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Breast Cancer Diagnosis Using Extended-Wavelength–Diffuse Reflectance Spectroscopy (EW-DRS) : Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning
- 2023
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Ingår i: Diagnostics. - 2075-4418. ; 13:19, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique.
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| 10. |
- Cossío, Fernando, et al.
(författare)
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VAI-B: A multicenter platform for the external validation of artificial intelligence algorithms in breast imaging
- 2023
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Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data. Approach: We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data onpremises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes. Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database. Conclusions: We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
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