The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Chiorean, Diana Maria; Mitranovici, Melinda-Ildiko; Mureșan, Maria Cezara; Buicu, Corneliu Florin; Moraru, Raluca; Moraru, Liviu; Cotoi, Titiana Cornelia; Cotoi, Ovidiu Simion; Apostol, Adrian; Turdean, Sabin Gligore; Mărginean, Cristina Oana; Petre, Ion; Oală, Ioan Emilian; Simon-Szabó, Zsuzsánna; Ivan, Viviana; Roșca, Ancuța Noela; Toru, Havva Serap

doi:10.3390/medicina59040672

Cited by 3 publications

(2 citation statements)

References 34 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence technology delivers precise and sensitive image analysis results, greatly benefiting breast cancer management. Currently, many studies have applied AI in various aspects of breast cancer and even other cancers' management [33][34][35], including digital pathology [36], clinical prognosis, and biopsy result analysis, among others. It is worth mentioning that although the imaging source used in this study is based on DBT images of breast cancer tumor tissues, the principle of determining margins is not unique to breast cancer.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

Shia,

Kuo,

Hsu

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery. Methods: this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models. Results: Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%. Conclusions: The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

Shia,

Kuo,

Hsu

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

“…In the early stages of cervical cancer, patients are largely asymptomatic, but few of those who are symptomatic, accuse lower abdominal pain, postcoital vaginal bleeding, abnormal menstrual bleeding, postmenopausal bleeding, vaginal discharge and disappearance [62].…”

Section: Discussionmentioning

confidence: 99%

Cervical Cancer or Cervical Endometriosis – Review and Case Report

Gobjila,

Anastasiu,

Petre

et al. 2023

Preprint

View full text Add to dashboard Cite

According to cancer death rates for women worldwide, this form of cancer ranks fourth after breast, bronchopulmonary, and colorectal cancer, affecting around 570,000 women annually. About 270,000 women each year pass away from this illness, 85% of them are from underdeveloped nations where cervical cancer claims more lives than any other kind of cancer. In Romania, cervical cancer ranks fourth in terms of death, behind breast, bronchopulmonary, and colorectal cancers, and second in terms of incidence, behind breast cancer. Endometriosis in the cervical region is quite uncommon. Only a tiny fraction of women had the condition upon diagnosis. It might be challenging to detect cervical endometriosis since it rarely manifests any symptoms or markers. Women who are asymptomatic can manage the condition expectantly; however, if the symptoms return often, surgery may be required. As many patients have concurrent diseases such fibroids, adenomyosis, ovarian cysts, and pelvic endometriosis, cervical endometriosis therapy appears to be straightforward. There ought to be surgical intervention. Symptomatic cervical endometriosis, such as irregular or post-coital bleeding, may be treated surgically using the LLETZ surgery or another cervical ablation approach. Background and Objectives: (1) To assess one's degree of knowledge on the signs, causes, and methods of preventing cervical cancer or cervical endometriosis, as well as screening for these conditions. (2) To ascertain how the responder behaves about cervical cancer screening and prevention, as well as endometriosis in the cervical region. Materials and Methods: A case report about cervical cancer or cervical endometriosis was conducted among a patient from Romania, Timisoara. Results: In this study, we report the patient with cervical endometriosis, although he was initially diagnosed with cervical cancer. Conclusions: Cervical cancer, a condition that can have serious repercussions on the patient's health, even if there is no clear picture of paraclinical investigations, to raise the suspicion of cervical endometriosis and not cervical cancer.

show abstract

Artificial Intelligence Algorithms in Predictive Factors for Hematologic Toxicities During Concurrent Chemoradiation for Cervical Cancer

Petre,

Negru,

Dragomir

et al. 2024

Cureus

View full text Add to dashboard Cite

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Cited by 3 publications

References 34 publications

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

Cervical Cancer or Cervical Endometriosis – Review and Case Report

Artificial Intelligence Algorithms in Predictive Factors for Hematologic Toxicities During Concurrent Chemoradiation for Cervical Cancer

Contact Info

Product

Resources

About