Could the serial determination of Ca15.3 serum improve the diagnostic accuracy of PET/CT? Results from small population with previous breast cancer

10570 Background: To evaluate if PET-MRI of breast tumors improves staging of breast cancer and obviates unnecessary breast biopsies. Methods: 106 patients with breast tumors detected by mammography or ultrasound and classified as BIRADS 3-5 were included in this IRB approved prospective study. All patients were examined with dedicated 18FDG-PET-CT and 3T multiparametric MRI of the breast. Examinations were scheduled no longer than 7 days apart. MRI protocol included: a diffusion-weighted sequence (DWI), a T2-w sequence and a contrast-enhanced combined high temporal and spatial resolution 3D-T1-w sequence before and after application of a standard dose Gd-DOTA. For PET-CT patients fasted at least 6 h before injection of approx. 300 MBq 18F-FDG based on the patients weight. Scanning was started 45 min after injection. Blood glucose levels were <150 mg/dl. A prone PET dataset over the breasts was acquired using a positioning device allowing the same patient geometry as the breast MRI coil. CT data was used for attenuation correction. Co-registration of imaging data and image fusion were performed. PET-MRI was assessed for lesion morphology and EH-kinetics according to BI-RADS and restricted diffusivity with an ADC threshold 1.25 x10-3mm2/s set as the cut-off for malignancy. Lesions were assed for 18F-FDG–avidity and classified as positive when 18F-FDG-uptake was greater than blood-pool activity. Additionally, nodal status was recorded for each technique and patient. Histopathology was used as the standard of reference. Results: PET-MRI achieved an excellent sensitivity of 100%. Specificity was increased from 68% to 80% as compared to routinely used MRI. Diagnostic accuracy of PET-MRI for diagnosis of breast cancer was 95%. PET-MRI would have obviated unnecessary breast biopsies in 80% of benign breast lesions without missing any cancers. Additionally, PET-MRI increased sensitivity in the detection of lymphnode metastases from 70% to 87% compared to MRI alone. Conclusions: With PET-MRI unnecessary breast biopsies can be obviated without missing any cancers. PET-MRI increases overall diagnostic accuracy in the diagnosis of breast cancer and lymphnode metastases for an accurate staging.

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P152 Diagnostic accuracy of axillary lymph node metastasis using FDG PET/CT in stage I/II breast cancer

The Breast ◽

10.1016/s0960-9776(15)70194-3 ◽

2015 ◽

Vol 24 ◽

pp. S77

Author(s):

Masahiro Ito ◽

Masatoshi Ito ◽

X. Duan ◽

H. Hirakawa ◽

M. Amari ◽

...

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Diagnostic Accuracy ◽

Axillary Lymph Node ◽

Axillary Lymph Node Metastasis ◽

Axillary Lymph ◽

Node Metastasis ◽

Pet Ct ◽

Fdg Pet Ct

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Benefits and harms of implementing [18F]FDG-PET/CT for diagnosing recurrent breast cancer: a prospective clinical study

EJNMMI Research ◽

10.1186/s13550-021-00833-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marianne Vogsen ◽

Jeanette Dupont Jensen ◽

Oke Gerke ◽

Anne Marie Bak Jylling ◽

Jon Thor Asmussen ◽

...

Keyword(s):

Breast Cancer ◽

Diagnostic Accuracy ◽

Fdg Pet ◽

Incidental Findings ◽

Posterior Probability ◽

Distant Metastases ◽

Recurrent Breast Cancer ◽

Pet Ct ◽

Recurrent Breast ◽

Fdg Pet Ct

Abstract Background [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography ([18F]FDG-PET/CT) has been implemented sporadically in hospital settings as the standard of care examination for recurrent breast cancer. We aimed to explore the clinical impact of implementing [18F]FDG-PET/CT for patients with clinically suspected recurrent breast cancer and validate the diagnostic accuracy. Methods Women with suspected distant recurrent breast cancer were prospectively enrolled in the study between September 2017 and August 2019. [18F]FDG-PET/CT was performed, and the appearance of incidental benign and malignant findings was registered. Additional examinations, complications, and the final diagnosis were registered to reflect the clinical consequence of such findings. The diagnostic accuracy of [18F]FDG-PET/CT as a stand-alone examination was analyzed. Biopsy and follow-up were used as a reference standard. Results [18F]FDG-PET/CT reported breast cancer metastases in 72 of 225 women (32.0%), and metastases were verified by biopsy in 52 (52/225, 23.1%). Prior probability and posterior probability of a positive test for suspected metastatic cancer and incidental malignancies were 27%/85% and 4%/20%, respectively. Suspected malignant incidental findings were reported in 46 patients (46/225, 20.4%), leading to further examinations and final detection of nine synchronous cancers (9/225, 4.0%). These cancers originated from the lung, thyroid, skin, pancreas, peritoneum, breast, kidney, one was malignant melanoma, and one was hematological cancer. False-positive incidental malignant findings were examined in 37/225 patients (16.4%), mainly in the colon (n = 12) and thyroid gland (n = 12). Ten incidental findings suspicious for benign disease were suggested by [18F]FDG-PET/CT, and further examinations resulted in the detection of three benign conditions requiring treatment. Sensitivity, specificity, and AUC-ROC for diagnosing distant metastases were 1.00 (0.93–1.0), 0.88 (0.82–0.92), and 0.98 (95% CI 0.97–0.99), respectively. Conclusion [18F]FDG-PET/CT provided a high posterior probability of positive test, and a negative test was able to rule out distant metastases in women with clinically suspected recurrent breast cancer. One-fifth of patients examined for incidental findings detected on [18F]FDG-PET/CT were diagnosed with clinically relevant conditions. Further examinations of false-positive incidental findings in one of six women should be weighed against the high accuracy for diagnosing metastatic breast cancer. Trial registration Clinical.Trials.gov. NCT03358589. Registered 30 November 2017—Retrospectively registered, http://www.ClinicalTrials.gov

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Comparison of diagnostic accuracy of 18F-fluoroestradiol and 18F-fluorodeoxyglucose positron emission tomography/computed tomography for breast cancer recurrence in patients with a history of estrogen receptor-positive primary breast cancer

10.21203/rs.3.rs-18613/v1 ◽

2020 ◽

Author(s):

Sun Young Chae ◽

Hye Joo Son ◽

Dong Yun Lee ◽

Eonwoo Shin ◽

Jungsu S. Oh ◽

...

Keyword(s):

Breast Cancer ◽

Diagnostic Accuracy ◽

Fdg Pet ◽

Primary Breast Cancer ◽

Cancer Recurrence ◽

Positron Emission ◽

Pet Ct ◽

Er Positive ◽

18F Fdg ◽

Fdg Pet Ct

Abstract Background To compare the diagnostic accuracy of 18F-fluoroestradiol (18F-FES) and 18F-fluorodoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for breast cancer recurrence in patients with estrogen receptor (ER)-positive primary breast cancer.Methods Our database of consecutive patients enrolled in a previous prospective cohort study to assess 18F-FES PET/CT was reviewed to identify eligible patients who had ER-positive primary breast cancer with suspected first recurrence at presentation and who underwent 18F-FDG PET/CT. The diagnostic accuracy of qualitative 18F-FES and 18F-FDG PET/CT interpretations was assessed, comparing them with histological diagnoses.Results Of the 46 enrolled patients, 45 were confirmed as having recurrent breast cancer, while one was diagnosed with chronic granulomatous inflammation. Forty (89%) patients were ER-positive, four (9%) were ER-negative, and one (2%) patient did not undergo an ER assay. The sensitivity of 18F-FES PET/CT was 71.1% (32/45, 95% CI: 55.7–83.6), while that of 18F-FDG PET/CT was 80.0% (36/45, 95% CI: 65.4–90.4) when malignant interpretation was defined as positive, and 93.3% (42/45, 95% CI: 81.7–98.6) when an equivocal or malignant interpretation was considered positive. There was no significant difference in sensitivity between 18F-FES and 18F-FDG PET/CT (P=0.48) when malignant 18F-FDG interpretation was considered positive, but the sensitivity of 18F-FDG was significantly higher than 18F-FES (P=0.013) when equivocal or malignant interpretation was considered positive. One patient with a benign lesion showed negative 18F-FES but malignant 18F-FDG uptake.Conclusions The restaging of patients who had ER-positive primary breast cancer and present with recurrent disease may include 18F-FES PET/CT.

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Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer

EJNMMI Research ◽

10.1186/s13550-021-00751-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zongyao Li ◽

Kazuhiro Kitajima ◽

Kenji Hirata ◽

Ren Togo ◽

Junki Takenaka ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Lymph Node ◽

Diagnostic Accuracy ◽

Fdg Pet ◽

Learning Technologies ◽

Axillary Lymph ◽

Pet Ct ◽

Assisted Diagnosis ◽

Fdg Pet Ct

Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.

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