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Context: Cervical cancer is the second most common cancer in women. The liquid based cervical cytology (LBCC) is a useful tool of choice for screening cervical cancer. Aims: To train a convolutional neural network (CNN) to identify abnormal foci from LBCC smears. Settings and Design: We have chosen retrospective study design from archived smears of patients undergoing screening from cervical cancer by LBCC smears. Materials and Methods: 2816 images, each of 256 × 256 pixels, were prepared from microphotographs of these LBCC smears, which included 816 “abnormal” foci (low grade or high grade squamous intraepithelial lesion) and 2000 'normal' foci (benign epithelial cells and reactive changes). The images were split into three sets, Training, Testing, and Evaluation. A convolutional neural network (CNN) was developed with the python programming language. The CNN was trained with the Training dataset; performance was assayed concurrently with the Testing dataset. Two CNN models were developed, after 20 and 10 epochs of training, respectively. The models were then run on the Evaluation dataset. Statistical Analysis Used: A contingency table was prepared from the original image labels and the labels predicted by the CNN. Results: Combined assessment of both models yielded a sensitivity of 95.63% in detecting abnormal foci, with 79.85% specificity. The negative predictive value was high (99.19%), suggesting potential utility in screening. False positives due to overlapping cells, neutrophils, and debris was the principal difficulty met during evaluation. Conclusions: The CNN shows promise as a screening tool; however, for its use in confirmatory diagnosis, further training with a more diverse dataset will be required.

Introduction: The Pap stained cervical smear is a screening tool for cervical cancer. Commercial systems are used for automated screening of liquid based cervical smears. However, there is no image analysis software used for conventional cervical smears. The aim of this study was to develop and test the diagnostic accuracy of a software for analysis of conventional smears. Materials and Methods: The software was developed using Python programming language and open source libraries. It was standardized with images from Bethesda Interobserver Reproducibility Project. One hundred and thirty images from smears which were reported Negative for Intraepithelial Lesion or Malignancy (NILM), and 45 images where some abnormality has been reported, were collected from the archives of the hospital. The software was then tested on the images. Results: The software was able to segregate images based on overall nuclear: cytoplasmic ratio, coefficient of variation (CV) in nuclear size, nuclear membrane irregularity, and clustering. 68.88% of abnormal images were flagged by the software, as well as 19.23% of NILM images. The major difficulties faced were segmentation of overlapping cell clusters and separation of neutrophils. Conclusion: The software shows potential as a screening tool for conventional cervical smears; however, further refinement in technique is required.

Background : Fine needle aspiration cytology (FNAC) may be diagnostic in candidates with indeterminate solitary pulmonary nodules (SPNs) suspicious of bronchogenic carcinoma. Aims : The study was performed to evaluate the usefulness of computed tomography (CT)-guided FNAC in our centre. Materials and Methods : All the cases had a strong clinical suspicion of lung cancer, negative bronchoscopy, negative sputum cytology for malignant cells and acid fast bacilli. A thorough radiological evaluation was made to rule out primary malignancy elsewhere. Results : A total of 94 patients were studied in one year. May-Grόnwald-Giemsa stain was used for the smears. The cytological diagnosis was correlated with clinical-radiological follow-up and biopsy to arrive at a final diagnosis. The procedure had a high sensitivity and specificity. Chi-square test was used to calculate statistical significance. Tumor of more than three centimeter and immediate cytological assessment significantly increased the yield. Review of slides added two cases of malignancy that were missed initially. There were very few complications. Conclusions : CT-guided FNAC was an accurate and safe procedure for SPNs.

Context: Cervical cancer is the second most common cancer in women. The liquid based cervical cytology (LBCC) is a useful tool of choice for screening cervical cancer. Aims: To train a convolutional neural network (CNN) to identify abnormal foci from LBCC smears. Settings and Design: We have chosen retrospective study design from archived smears of patients undergoing screening from cervical cancer by LBCC smears. Materials and Methods: 2816 images, each of 256 × 256 pixels, were prepared from microphotographs of these LBCC smears, which included 816 "abnormal" foci (low grade or high grade squamous intraepithelial lesion) and 2000 'normal' foci (benign epithelial cells and reactive changes). The images were split into three sets, Training, Testing, and Evaluation. A convolutional neural network (CNN) was developed with the python programming language. The CNN was trained with the Training dataset; performance was assayed concurrently with the Testing dataset. Two CNN models were developed, after 20 and 10 epochs of training, respectively. The models were then run on the Evaluation dataset. Statistical Analysis Used: A contingency table was prepared from the original image labels and the labels predicted by the CNN. Results: Combined assessment of both models yielded a sensitivity of 95.63% in detecting abnormal foci, with 79.85% specificity. The negative predictive value was high (99.19%), suggesting potential utility in screening. False positives due to overlapping cells, neutrophils, and debris was the principal difficulty met during evaluation. Conclusions: The CNN shows promise as a screening tool; however, for its use in confirmatory diagnosis, further training with a more diverse dataset will be required. [ABSTRACT FROM AUTHOR]

Introduction: The Pap stained cervical smear is a screening tool for cervical cancer. Commercial systems are used for automated screening of liquid based cervical smears. However, there is no image analysis software used for conventional cervical smears. The aim of this study was to develop and test the diagnostic accuracy of a software for analysis of conventional smears. Materials and Methods: The software was developed using Python programming language and open source libraries. It was standardized with images from Bethesda Interobserver Reproducibility Project. One hundred and thirty images from smears which were reported Negative for Intraepithelial Lesion or Malignancy (NILM), and 45 images where some abnormality has been reported, were collected from the archives of the hospital. The software was then tested on the images. Results: The software was able to segregate images based on overall nuclear: cytoplasmic ratio, coefficient of variation (CV) in nuclear size, nuclear membrane irregularity, and clustering. 68.88% of abnormal images were flagged by the software, as well as 19.23% of NILM images. The major difficulties faced were segmentation of overlapping cell clusters and separation of neutrophils. Conclusion: The software shows potential as a screening tool for conventional cervical smears; however, further refinement in technique is required. [ABSTRACT FROM AUTHOR]