A practical guide to managing CT findings in the breast

Al‐Katib, Sayf; Gupta, Gurpriya; Brudvik, Andrea; Ries, Stacy; Krauss, J.; Farah, Michael

doi:10.1016/j.clinimag.2018.07.009

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…It was found that the segmentation accuracy, precision, and recall rate of the algorithm in this study were significantly greater than those of the SE-Res Block U-shaped CNN algorithm and density peak clustering algorithm (P < 0.05). Such results were similar to the research results of Al-Katib et al [21], indicating that the CNN segmentation algorithm used in this study showed better segmentation performance for CT images than traditional algorithms and had clinical application value.…”

Section: Discussionsupporting

confidence: 91%

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

Zhu

et al. 2021

Scientific Programming

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This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P < 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.

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Section: Discussionsupporting

confidence: 91%

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

Zhu

et al. 2021

Scientific Programming

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“…However, the number of newly registered breast cancers increased, without reaching statistical significance (p=0.41), although in Romania there are no screening programs for breast cancer. CT studies for the diagnosis of SARS-CoV-2 pneumonia incidentally found macro calcifications and tumor masses in the breasts [16], which therefore increased the number of newly registered cases.…”

Section: Discussionmentioning

confidence: 99%

Management of Cancer Patients During the SARS-CoV-2 Pandemic, in a County in Romania

Leşe¹,

Micu-Chiş²,

Leşe³

2021

EJPM

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Introduction. The SARS-CoV-2 pandemic has profoundly affected health systems by relocating resources, enforcing restrictive measures and burdening health care workers. The study aims to assess the changes in the treatment and the evolution of cancer patients in a county in Romania, with a population of over a half million inhabitants. Patients and method. Patients with breast, gastric, colorectal, and lung cancer were studied over a two-year period: 14 months before the onset of the pandemic and 10 months after its beginning. The number of new cases of cancer, the number of chemotherapy and radiotherapy sessions, the number of surgeries and deaths from these four types of cancer were recorded. Results. During the pandemic, fewer chemotherapy sessions were administered for all cancers (p<0.0001). The number of radiotherapy sessions for breast (p<0.007) and colorectal cancer (p<0.002) increased by banning patients from going to university hospitals and decreased for lung cancer (p<0.03) which has symptoms similar to those of the SARS-CoV-2 virus. Conclusions. Cancer patients had limited access to chemotherapy. Their diagnosis and treatment were included in the same category as non-lifethreatening diseases. Testing, quarantine and travel limitation were not accompanied by additional safe medical services for the oncological patients. The death rate is inconclusive because the time since the onset of the pandemic is shorter than the natural evolution of the types of cancers studied.

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“…Computed tomography (CT) has always been considered an inaccurate method for breast evaluation; however many studies have shown that conventional chest CT is able to detect malignant breast lesions 3–10 . Dedicated CT devices developed for breast assessment, using technologies, such as “cone beam” and “phase contrast,” have shown promising results 11–14 .…”

mentioning

confidence: 99%

“…1,2 Computed tomography (CT) has always been considered an inaccurate method for breast evaluation; however many studies have shown that conventional chest CT is able to detect malignant breast lesions. [3][4][5][6][7][8][9][10] Dedicated CT devices developed for breast assessment, using technologies, such as "cone beam" and "phase contrast," have shown promising results. [11][12][13][14] However, only few studies have evaluated the performance of conventional multide-tector CT (MDCT) devices in the evaluation of breast lesions (Table 1).…”

mentioning

confidence: 99%

Iodinated Contrast Enhancement of Breast Cancer on Prone Multidetector Computed Tomography—Preliminary Findings

Felipe

Barbosa

Chojniak

et al. 2022

J Comput Assist Tomogr

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A practical guide to managing CT findings in the breast

Cited by 6 publications

References 25 publications

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

Management of Cancer Patients During the SARS-CoV-2 Pandemic, in a County in Romania

Iodinated Contrast Enhancement of Breast Cancer on Prone Multidetector Computed Tomography—Preliminary Findings

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