Cost and pollution by the use of xylene in cervical cytology in four Peruvian hospitals

Moya‐Salazar, Jeel; Salazar-Hernández, Richard; Rojas‐Zumaran, Víctor; Cruz-Gonzales, Gloria; Contreras‐Pulache, Hans

doi:10.12688/f1000research.52769.3

Cited by 2 publications

(2 citation statements)

References 23 publications

(31 reference statements)

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“…On the other hand, it is expensive to construct and maintain a certificated cytology laboratory with precision facilities and trained personnel 11 . Along with a number of cytology tests per year, the high consumption of hazardous reagents, such as xylene, also poses high economic costs in storage and disposal, as well as chemical hazards to pathologists 12,13 .…”

Section: Introductionmentioning

confidence: 99%

Label-free virtual peritoneal lavage cytology via deep-learning-assisted single-color stimulated Raman scattering microscopy

Fang,

Wu,

Chen

et al. 2024

Preprint

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Clinical guidelines for gastric cancer treatment recommend intraoperative peritoneal lavage cytology to detect free cancer cells. Patients with positive cytology require neoadjuvant chemotherapy instead of instant resection and conversion to negative cytology results in improved survival. However, the accuracy of cytological diagnosis by pathologists or artificial intelligence is disturbed by manually-produced, unstandardized slides. In addition, the elaborate infrastructure makes cytology accessible to a limited number of medical institutes. Here, we developed CellGAN, a deep learning method that enables label-free virtual peritoneal lavage cytology by producing virtual hematoxylin-eosin-stained images with single-color stimulated Raman scattering microscopy. A structural similarity loss was introduced to overcome the challenge of existing unsupervised virtual pathology techniques unable to present cellular structures accurately. This method achieved a structural similarity of 0.820±0.041 and a nucleus area consistency of 0.698±0.102, indicating the staining fidelity outperforming the state-of-the-art method. Diagnosis using virtually stained cells reached 93.8% accuracy and substantial consistency with conventional staining. Single-cell detection and classification on virtual slides achieved a mean average precision of 0.924 and an area under the receiver operating characteristic curve of 0.906, respectively. Collectively, this method achieves standardized and accurate virtual peritoneal lavage cytology and holds great potential for clinical translation.

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Section: Introductionmentioning

confidence: 99%

Label-free virtual peritoneal lavage cytology via deep-learning-assisted single-color stimulated Raman scattering microscopy

Fang,

Wu,

Chen

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[ 11 ] Along with plentiful cytology tests per year, the high consumption of hazardous reagents poses high storage and disposal costs and health risks to pathologists. [ 12,13 ]…”

Section: Introductionmentioning

confidence: 99%

Label‐Free Virtual Peritoneal Lavage Cytology via Deep‐Learning‐Assisted Single‐Color Stimulated Raman Scattering Microscopy

Fang,

Wu,

Chen

et al. 2024

Advanced Intelligent Systems

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Clinical guidelines for gastric cancer treatment recommend intraoperative peritoneal lavage cytology to detect free cancer cells. Patients with positive cytology require neoadjuvant chemotherapy instead of instant resection, and conversion to negative cytology results in improved survival. However, pathologists’ or artificial intelligence's accuracy of cytological diagnosis is disturbed by manually produced, unstandardized slides. In addition, the elaborate infrastructure makes cytology accessible to a limited number of medical institutes. This work develops CellGAN, a deep learning method that enables label‐free virtual peritoneal lavage cytology by producing virtual hematoxylin–eosin‐stained images with single‐color stimulated Raman scattering microscopy. A structural similarity loss is introduced to overcome the challenge of unsupervised virtual pathology techniques that cannot accurately present cellular structures. This method achieves a structural similarity of 0.820 ± 0.041 and a nucleus area consistency of 0.698 ± 0.102, indicating the staining fidelity outperforms the state‐of‐the‐art method. Diagnosis using virtually stained cells reaches 93.8% accuracy and substantial consistency with conventional staining. Single‐cell detection and classification on virtual slides achieve a mean average precision of 0.924 and an area under the receiver operating characteristic curve of 0.906, respectively. Collectively, this method achieves standardized and accurate virtual peritoneal lavage cytology and holds great potential for clinical translation.

show abstract

Cost and pollution by the use of xylene in cervical cytology in four Peruvian hospitals

Cited by 2 publications

References 23 publications

Label-free virtual peritoneal lavage cytology via deep-learning-assisted single-color stimulated Raman scattering microscopy

Label-free virtual peritoneal lavage cytology via deep-learning-assisted single-color stimulated Raman scattering microscopy

Label‐Free Virtual Peritoneal Lavage Cytology via Deep‐Learning‐Assisted Single‐Color Stimulated Raman Scattering Microscopy

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