Automatic screening of cytological smears for cancer: the instrumentation

Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

Applying Artificial Intelligence to Gynecologic Oncology: A Review

Importance

Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies.

Objective

The aim of this study was to review the role of AI in gynecologic oncology.

Evidence Acquisition

Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers.

Results

A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response.

Conclusions and Relevance

Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Cervical cancer: Automation of Pap test screening

BACKGROUND

Cervical cancer progresses slowly, increasing the chance of early detection of pre-neoplastic lesions via Pap exam test and subsequently preventing deaths. However, the exam presents both false-negatives and false-positives results. Therefore, automatic methods (AMs) of reading the Pap test have been used to improve the quality control of the exam. We performed a literature review to evaluate the feasibility of implementing AMs in laboratories.

METHODS

This work reviewed scientific publications regarding automated cytology from the last 15 years. The terms used were "Papanicolaou test" and "Automated cytology screening" in Portuguese, English, and Spanish, in the three scientific databases (SCIELO, PUBMED, MEDLINE).

RESULTS

Of the resulting 787 articles, 34 were selected for a complete review, including three AMs: ThinPrep Imaging System, FocalPoint GS Imaging System and CytoProcessor. In total, 1 317 148 cytopathological slides were evaluated automatically, with 1 308 028 (99.3%) liquid-based cytology slides and 9120 (0.7%) conventional cytology smears. The AM diagnostic performances were statistically equal to or better than those of the manual method. AM use increased the detection of cellular abnormalities and reduced false-negatives. The average sample rejection rate was ≤3.5%.

CONCLUSION

AMs are relevant in quality control during the analytical phase of cervical cancer screening. This technology eliminates slide-handling steps and reduces the sample space, allowing professionals to focus on diagnostic interpretation while maintaining high-level care, which can reduce false-negatives. Further studies with conventional cytology are needed. The use of AM is still not so widespread in cytopathology laboratories.

Automatic screening of cervical cells using block image processing

Background

Cervical cancer is the second leading cause of female-specific cancer-related deaths after breast cancer, especially in developing countries. However, the incidence of the disease may be significantly decreased if the patient is diagnosed in the pre-cancerous lesion stage or earlier. In recent years, computer-based algorithms are widely used in cervical cancer screening. Most of the proposed algorithms follow the procedure of segmentation, feature extraction, and then classification. Nevertheless, few of the existing segmentation methods are as flexible and robust as the human visual system, and the complexity of the algorithms makes it difficult for clinical application.

Methods

In this study, a computer-assisted analytical approach is proposed to identify the existence of suspicious cells in a whole slide cervical cell image (WSCCI). The main difference between our method and the conventional algorithm is that the image is divided into blocks with certain size instead of segmented cells, which can greatly reduce the computational complexity. Via data analysis, some texture and color histogram features show significant differences between blocks with and without suspicious cells. Therefore these features can be used as the input of the support vector machine classifier. 1100 non-background blocks (110 suspicious blocks) are trained to build a model, while 1040 blocks (491 non-background blocks) from 12 other WSCCIs are tested to verify the feasibility of the algorithm.

Results

The experimental results show that the accuracy of our method is about 98.98 %. More importantly, the sensitivity, which is more fatal in cancer screening, is 95.0 % according to the images tested in the study, while the specificity is 99.33 %.

Conclusion

The analysis of the algorithm is based on block images, which is different from conventional methods. Although some analysis work should be done in advance, the later processing speed will be greatly enhanced with the establishment of the model. Furthermore, since the algorithm is based on the actual WSCCI, the method will be of directive significance for clinical screening.

Screening for cervical cancer using automated analysis of PAP-smears

Cervical cancer is one of the most deadly and common forms of cancer among women if no action is taken to prevent it, yet it is preventable through a simple screening test, the so-called PAP-smear. This is the most effective cancer prevention measure developed so far. But the visual examination of the smears is time consuming and expensive and there have been numerous attempts at automating the analysis ever since the test was introduced more than 60 years ago. The first commercial systems for automated analysis of the cell samples appeared around the turn of the millennium but they have had limited impact on the screening costs. In this paper we examine the key issues that need to be addressed when an automated analysis system is developed and discuss how these challenges have been met over the years. The lessons learned may be useful in the efforts to create a cost-effective screening system that could make affordable screening for cervical cancer available for all women globally, thus preventing most of the quarter million annual unnecessary deaths still caused by this disease.

A note from history: Landmarks in history of cancer, Part 6

In the 3 decades from 1940 to 1970, the United States became the nucleus for research, diagnosis, and treatment of cancer. The discovery of anticancer drugs, and the clinical demonstration that chemotherapy and radiation can cure cancer and have the ability to prevent recurrence of cancer, were incontrovertibly the most remarkable groundbreaking events. Consequently, the trend of less surgery and more multimodality therapy began. The introduction of radioautography, mammography, ultrasonography, computed tomography, Papanicolaou smear, and other novel laboratory tests furthered early detection of cancer and refined accurate diagnosis. The unequivocal linking of lung cancer to cigarette smoking made medical history. The delineation of the potential role of oncogenes adduced new thoughts about oncogenesis and cancer prevention, and pathologists finalized the classification and nosology of tumors. Finally, it is worth noting that although more advances were made in the detection, diagnosis, and treatment of cancers than any other period in history, the overall mortality rate of patients with cancer remained high and unchanged.

Cervical cytology automation: an update for 2003. The end of the quest nears?

The field of cytology automation, through long investigation, trial and error, and finally, commercial success and failure, has arrived at the first levels of the "grail" of improvements in accuracy and productivity in cervical cytology screening. It remains to be seen how much further the road will lead toward so-called "diagnostic" instrumentation that would actually provide us with a fully automated system of "specimen in-diagnosis out" with little, or no, human input. Will commercial ventures or academic institutions continue to support investigations to further the applications that have been developed to date? This remains to be seen and is directly dependent on parallel processes that are detailed elsewhere in this issue. Will HPV vaccines eliminate the need for screening? Possibly, but probably not for many years [70]. Will more sensitive and specific genetic or protein markers (or combinations thereof) be found to be more accurate and cost-effective? Certainly the possibility of mass screening by high-risk HPV DNA testing, as a viable alternative, is being discussed at present. Despite all of these uncertainties, the present (or nearly available) technology has the potential to improve the practice of cervical cytology. Improvements in accuracy that are necessary to provide the highest possible level of patient care and to protect practitioners from unreasonable levels of medico-legal risk are a reality. Improvements in productivity that are necessary to help in the impending labor shortage in the field of cytotechnology are also a reality. Automation is clearly the short-term solution to the most difficult of the challenges that we face.

Machine recognition in medical microscopy

Some general aspects of machine recognition are discussed. The advantages and limitations of machine systems are compared with those of trained human observers and the problems involved in introducing machines into classification systems designed for human observers are outlined.

The present classification system for the cellular components of blood samples is described, with emphasis on the different types of white cell encountered. Cervical samples and chromosomes are described and discussed from the machine recognition point of view.

All proposed machine recognition systems in microscopy must satisfy certain technical criteria; these are listed and discussed briefly.

A number of earlier attempts to recognise cells are described; the results obtained and the difficulties encountered are outlined.

The work of a number of university, medical and industrial research groups actively engaged in this field is described.

The likely developments in this field are discussed from the point of view of improvements which may occur in the hardware of scanning and computing and also the better understanding, integration and exploration of machine systems in medical research and in routine medical diagnostic procedures.