Diagnostics

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14 pages, 4110 KiB

Open AccessArticle

An Unenhanced Breast MRI Protocol Based on Diffusion-Weighted Imaging: A Retrospective Single-Center Study on High-Risk Population for Breast Cancer

by Anna Rotili, Filippo Pesapane, Giulia Signorelli, Silvia Penco, Luca Nicosia, Anna Bozzini, Lorenza Meneghetti, Cristina Zanzottera, Sara Mannucci, Bernardo Bonanni and Enrico Cassano

Diagnostics 2023, 13(12), 1996; https://doi.org/10.3390/diagnostics13121996 - 7 Jun 2023

Cited by 1 | Viewed by 2137

Abstract

Purpose: This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population. Methods: MR [...] Read more.

Purpose: This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population. Methods: MR images from asymptomatic women, carriers of a germline mutation in either the BRCA1 or BRCA2 gene, collected in a single center from January 2019 to December 2021 were retrospectively evaluated. A radiologist with experience in breast imaging (R1) and a radiology resident (R2) independently evaluated DWI/ADC maps and, in case of doubts, T2-WI. The standard of reference was the pathological diagnosis through biopsy or surgery, or ≥1 year of clinical and radiological follow-up. Diagnostic performances were calculated for both readers with a 95% confidence interval (CI). The agreement was assessed using Cohen’s kappa (κ) statistics. Results: Out of 313 women, 145 women were included (49.5 ± 12 years), totaling 344 breast MRIs with DWI/ADC maps. The per-exam cancer prevalence was 11/344 (3.2%). The sensitivity was 8/11 (73%; 95% CI: 46–99%) for R1 and 7/11 (64%; 95% CI: 35–92%) for R2. The specificity was 301/333 (90%; 95% CI: 87–94%) for both readers. The diagnostic accuracy was 90% for both readers. R1 recalled 40/344 exams (11.6%) and R2 recalled 39/344 exams (11.3%). Inter-reader reproducibility between readers was in moderate agreement (κ = 0.43). Conclusions: In female carriers of a BRCA1/2 mutation, breast DWI supplemented with T2-WI allowed breast cancer detection with high sensitivity and specificity by a radiologist with extensive experience in breast imaging, which is comparable to other screening tests. The findings suggest that DWI and T2-WI have the potential to serve as a stand-alone method for unenhanced breast MRI screening in a selected population, opening up new perspectives for prospective trials. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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17 pages, 1362 KiB

Open AccessArticle

5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study

by Tian He, Yin-Ying Pu, Ya-Qin Zhang, Zhe-Bin Qian, Le-Hang Guo, Li-Ping Sun, Chong-Ke Zhao and Hui-Xiong Xu

Diagnostics 2023, 13(3), 362; https://doi.org/10.3390/diagnostics13030362 - 18 Jan 2023

Cited by 8 | Viewed by 2250

Abstract

Objective: Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective breast US screening is lacking in rural and remote areas. To alleviate this issue, we prospectively evaluated the clinical availability of 5G-based telerobotic US technology for [...] Read more.

Objective: Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective breast US screening is lacking in rural and remote areas. To alleviate this issue, we prospectively evaluated the clinical availability of 5G-based telerobotic US technology for breast examinations in rural and remote areas. Methods: Between September 2020 and March 2021, 63 patients underwent conventional and telerobotic US examinations in a rural island (Scenario A), while 20 patients underwent telerobotic US examination in a mobile car located in a remote county (Scenario B) in May 2021. The safety, duration, US image quality, consistency, and acceptability of the 5G-based telerobotic US were assessed. Results: In Scenario A, the average duration of the telerobotic US procedure was longer than that of conventional US (10.3 ± 3.3 min vs. 7.6 ± 3.0 min, p = 0.017), but their average imaging scores were similar (4.86 vs. 4.90, p = 0.159). Two cases of gynecomastia, one of lactation mastitis, and one of postoperative breast effusion were diagnosed and 32 nodules were detected using the two US methods. There was good interobserver agreement between the US features and BI-RADS categories of the identical nodules (ICC = 0.795–1.000). In Scenario B, breast nodules were detected in 65% of the patients using telerobotic US. Its average duration was 10.1 ± 2.3 min, and the average imaging score was 4.85. Overall, 90.4% of the patients were willing to choose telerobotic US in the future, and tele-sonologists were satisfied with 85.5% of the examinations. Conclusion: The 5G-based telerobotic US system is feasible for providing effective breast examinations in rural and remote areas. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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13 pages, 1905 KiB

Open AccessArticle

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

by Grace Ugochi Nneji, Happy Nkanta Monday, Goodness Temofe Mgbejime, Venkat Subramanyam R. Pathapati, Saifun Nahar and Chiagoziem Chima Ukwuoma

Diagnostics 2023, 13(2), 299; https://doi.org/10.3390/diagnostics13020299 - 13 Jan 2023

Cited by 6 | Viewed by 1857

Abstract

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when [...] Read more.

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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14 pages, 4896 KiB

Open AccessArticle

Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning

by Yuxuan Che, Fei Ren, Xueyuan Zhang, Li Cui, Huanwen Wu and Ze Zhao

Diagnostics 2023, 13(2), 263; https://doi.org/10.3390/diagnostics13020263 - 10 Jan 2023

Cited by 9 | Viewed by 5054

Abstract

Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 [...] Read more.

Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation criteria mainly includes three indexes: staining intensity, circumferential membrane staining pattern, and proportion of positive cells. Manually scoring HER2 IHC images is an error-prone, variable, and time-consuming work. To solve these problems, this study proposes an automated predictive method for scoring whole-slide images (WSI) of HER2 slides based on a deep learning network. A total of 95 HER2 pathological slides from September 2021 to December 2021 were included. The average patch level precision and f1 score were 95.77% and 83.09%, respectively. The overall accuracy of automated scoring for slide-level classification was 97.9%. The proposed method showed excellent specificity for all IHC 0 and 3+ slides and most 1+ and 2+ slides. The evaluation effect of the integrated method is better than the effect of using the staining result only. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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16 pages, 2203 KiB

Open AccessArticle

Vision-Transformer-Based Transfer Learning for Mammogram Classification

by Gelan Ayana, Kokeb Dese, Yisak Dereje, Yonas Kebede, Hika Barki, Dechassa Amdissa, Nahimiya Husen, Fikadu Mulugeta, Bontu Habtamu and Se-Woon Choe

Diagnostics 2023, 13(2), 178; https://doi.org/10.3390/diagnostics13020178 - 4 Jan 2023

Cited by 45 | Viewed by 9139

Abstract

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have [...] Read more.

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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15 pages, 2178 KiB

Open AccessArticle

Assessment of Enhancement Kinetics Improves the Specificity of Abbreviated Breast MRI: Performance in an Enriched Cohort

by Haejung Kim, Eun Young Ko, Ka Eun Kim, Myoung Kyoung Kim, Ji Soo Choi, Eun Sook Ko and Boo-Kyung Han

Diagnostics 2023, 13(1), 136; https://doi.org/10.3390/diagnostics13010136 - 30 Dec 2022

Cited by 4 | Viewed by 3064 | Correction

Abstract

Objective: To investigate the added value of kinetic information for breast lesion evaluation on abbreviated breast MRI (AB-MRI). Methods: This retrospective study analyzed 207 breast lesions with Breast Imaging Reporting and Data System categories 3, 4, or 5 on AB-MRI in 198 consecutive [...] Read more.

Objective: To investigate the added value of kinetic information for breast lesion evaluation on abbreviated breast MRI (AB-MRI). Methods: This retrospective study analyzed 207 breast lesions with Breast Imaging Reporting and Data System categories 3, 4, or 5 on AB-MRI in 198 consecutive patients who had breast MRI for screening after breast cancer surgery between January 2017 and December 2019. All lesions were pathologically confirmed or stable on follow-up images for 2 years or more. Kinetic information of the lesions regarding the degree and rate of enhancement on the first post-contrast-enhanced image and the enhancement curve type from two post-contrast-enhanced images were analyzed on a commercially available computer-assisted diagnosis system. The diagnostic performances of AB-MRI with morphological analysis alone and with the addition of kinetic information were compared using the McNemar test. Results: Of 207 lesions, 59 (28.5%) were malignant and 148 (71.5%) were benign. The addition of an enhancement degree of ≥90% to the morphological analysis significantly increased the specificity of AB-MRI (29.7% vs. 52.7%, p < 0.001) without significantly reducing the sensitivity (94.9% vs. 89.8%, p = 0.083) compared to morphological analysis alone. Unnecessary biopsy could have been avoided in 34 benign lesions, although three malignant lesions could have been missed. For detecting invasive cancer, adding an enhancement degree ≥107% to the morphological analysis significantly increased the specificity (26.5% vs. 57.6%, p < 0.001) without significantly decreasing the sensitivity (94.6% vs. 86.5%, p = 0.083). Conclusion: Adding the degree of enhancement on the first post-contrast-enhanced image to the morphological analysis resulted in higher AB-MRI specificity without compromising its sensitivity. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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10 pages, 2201 KiB

Open AccessArticle

The Frequency and Causes of Not-Detected Breast Malignancy in Dynamic Contrast-Enhanced MRI

by Donghun Song, Bong Joo Kang, Sung Hun Kim, Jeongmin Lee and Ga Eun Park

Diagnostics 2022, 12(11), 2575; https://doi.org/10.3390/diagnostics12112575 - 24 Oct 2022

Cited by 5 | Viewed by 1868

Abstract

Breast MR is the most sensitive imaging modality, but there are cases of malignant tumors that are not detected in MR. This study evaluated the frequency and main causes of malignant breast lesions not detected in dynamic contrast-enhanced (DCE) MR. A total of [...] Read more.

Breast MR is the most sensitive imaging modality, but there are cases of malignant tumors that are not detected in MR. This study evaluated the frequency and main causes of malignant breast lesions not detected in dynamic contrast-enhanced (DCE) MR. A total of 1707 cases of preoperative breast MR performed between 2020 and 2021 were included. Three radiologists individually reviewed the DCE MRs and found not-detected malignancy cases in the MRs. The final cases were decided through consensus. For the selected cases, images other than DCE MRIs, such as mammography, ultrasounds, diffusion-weighted MRs, and, if possible, contrast-enhanced chest CTs, were analyzed. In the final sample, 12 cases were not detected in DCE MR, and the frequency was 0.7% (12/1707). Six cases were not detected due to known non-enhancing histologic features. In four cases, tumors were located in the breast periphery and showed no enhancement in MR. In the remaining two cases, malignant lesions were not identified due to underlying marked levels of BPE. The frequency of not-detected malignancy in DCE MR is rare. Knowing the causes of each case and correlating it with other imaging modalities could be helpful in the diagnosis of breast malignancy in DCE MR. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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10 pages, 1882 KiB

Open AccessArticle

Comparison of the Ultrasound Visibility of Tissue Markers in Metastatic Lymph Nodes after Neoadjuvant Chemotherapy in Patients with Breast Cancer

by Ka Eun Kim, Eun Young Ko, Boo-Kyung Han, Eun Sook Ko, Ji Soo Choi, Haejung Kim, Jeong Eon Lee and Hyunwoo Lee

Diagnostics 2022, 12(10), 2424; https://doi.org/10.3390/diagnostics12102424 - 7 Oct 2022

Viewed by 2778

Abstract

This study aimed to investigate the differences in ultrasound (US) visibility for the localization of clipped metastatic lymph nodes after neoadjuvant chemotherapy (NAC), according to tissue marker type. This single-center retrospective study included 59 consecutive patients with breast cancer who underwent tissue marker [...] Read more.

This study aimed to investigate the differences in ultrasound (US) visibility for the localization of clipped metastatic lymph nodes after neoadjuvant chemotherapy (NAC), according to tissue marker type. This single-center retrospective study included 59 consecutive patients with breast cancer who underwent tissue marker insertion for histologically proven metastatic axillary lymph nodes before NAC, between March 2020 and August 2021. Two breast tissue markers were used: UltraClip™ (n = 29) and UltraCor™ Twirl™ (n = 30). The US visibility of tissue markers after NAC and the successful excision rate of the clipped lymph nodes were compared between the two types of tissue markers. UltraCor™ Twirl™ showed better overall US visibility than UltraClip™ after NAC (86.7% vs. 72.4%), but the difference was statistically insignificant. In the absence of residual metastatic lymph nodes on US after NAC (n = 32), UltraCor™ Twirl™ showed significantly better US visibility (83.3%, 15/18) than UltraClip™ (42.9%, 6/14; p = 0.027). The marker type was not associated with the successful excision of the clipped lymph node. UltraCor™ Twirl™ showed better US visibility than UltraClip™ in the metastatic axillary lymph nodes after NAC in the absence of residual suspicious lymph nodes on US. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Successes and Challenges)

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