Computer-assisted cervical cancer screening using neural networks

CANCER LETTERS

Cancer Letters 77 (1994) 155-162

Computer-assisted cervical cancer screening using neural networks

Laurie J. Mango

Neuromedical Systems, Inc.. Two Executive Blvd. Suffern, NY 10901. USA

(Received 27 September 1993; revision received 2 December 1993; accepted 14 December 1993)

Abstract

A practical and effective system for the computer-assisted screening of conventionally prepared cervical smears is

presented and described. Recent developments in neural network technology have made computerized analysis of the complex cellular scenes found on Pap smears possible. The PAPNEP Cytological Screening System uses neural networks to automatically analyze conventional smears by locating and recognizing potentially abnormal cells. It then

displays images of these objects for review and final diagnosis by qualified cytologists. The results of the studies presented indicate that the PAPNET system could be a useful tool for both the screening and rescreening of cervical smears. In addition, the system has been shown to be sensitive to some types of abnormalities which have gone undetected during manual screening.

Key words: Automated cytology; Cervical cytology; Pap smear; Neural networks; PAPNET; Screening

1. Introduction

Cervical smear screening for the presence of abnormality is widely recognized as one of the

greatest successes in oncologic prevention. Since its introduction in the early 1940s by George N. Papanicolaou [19], it has been instrumental in reducing cervical cancer mortality by more than

70% in the United States [6]. Nonetheless, the ‘Pap’ smear has serious limitations, as evidenced by its high false-negative rate. In fact, some studies have reported false-negative rates for cervical

cytology smears as high as 50% [ 151, and have shown that many false-negative diagnoses result from screening errors.

Reports in the lay press during the late 1980s of

these high false-negative rates exposed an over- burdened system fraught with errors [3]. This adverse media attention prompted the United States Congress to enact the Clinical Laboratory

Improvement Amendments of 1988. This legisla- tive act limits a cytotechnologist’s cervical-smear caseload and mandates proficiency testing of cytotechnologists. However, despite this new emphasis

on quality control in the laboratories, the false- negative rate of the cervical smear still remains unacceptably high. There is an extreme shortage of certified cytotechnologists available to manually screen smears. Despite consequent salary increases, the current entry rate of newly trained cytotech-

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156 L.J. Mango/Cancer Lett. 77 (1994) 155-162

nologists into the work force remains insufficient. Manually screening cervical smears is a fatigu-

ing, time-consuming and difficult task. It has been described as ‘a uniquely labor-intensive complex process, the outcome of which depends entirely on human judgment.. .‘[ 151. Demanding an individual’s full concentration, screening involves the microscopic search for the relatively few abnormal cells from hundreds of thousands of cells on a smear. As more than 90% of all cervical smears are ‘negative,’ psychological habituation easily occurs. Task-related errors are responsible for many of the false-negative diagnoses and are thought to be inherent to the process of manual screening. Such errors can contribute to patient death from cervical carcinoma, a theoretically preventable disease.

An obvious solution to the problem of screening errors is the automation of some or all of the screening process. While an estimated 73 million gynecologic tests are performed in the US each year [ 181, the cervical smear remains the only high- volume laboratory test which has not been widely automated. Automated cytological analysis could eliminate much of the habituation associated with manual screening, thereby considerably reducing the error rate.

Historical attempts to automate analysis of cervical smears date to the 1950s [ 141. While par- tially successful, these systems were not practical due to the extreme difficulty in reliably segmenting objects (cells and clusters) from the complex and overlapping scenes intrinsic to the conventional cervical smear. Smear preparations can also vary greatly in staining, thickness, background, and the presence of artifacts. Many stages and degrees of abnormality exist, representing a diverse set of morphologies. Hence, standard computer classification schemes (using a priori algorithmic ‘rules’) alone proved to be unsatisfactory for practical use.

To avoid some of the difficulties presented by conventional cervical smears, a fluid-preparation technique producing a single layer of cells, was proposed. Unfortunately this approach resulted in the loss of diagnostic cells through filtration, adhe- sion, and processing, as well as discarding impor- tant background clues needed for accurate cervical smear diagnosis. It also had the practical dis-

advantage of altering a successful and well- established clinical methodology. Computer analysis of the conventional smear, without change, remained an unmet challenge.

Neural networks are a non-algorithmic branch of artificial intelligence. They are parallel, connec- tionist computer systems inspired by neuro- biology. Neural networks have provided significant advantages over standard computer methods, especially for pattern recognition applications. Neural networks do not use rules, rather they ‘learn’ from ‘training sets,’ much the way people do. They can generalize and are ideal for highly variable pattern recognition. Recent ad- vances in neural networks have made automation of conventional cervical smear screening practical for the first time.

The PAPNET@ Cytological Screening system (Neuromedical Systems, Inc., Suffern, NY) represents a new approach for the computer- assisted analysis of conventional cervical smears. It serves as an adjunct to current practice by automating the search for potentially abnormal cells which are then interpreted by cytologists. The complete system includes two units: a scanner which is located in a central processing facility and a review station located in a pathology laboratory. The scanner uses neural networks to analyze each smear, identify 128 of the most abnormal appear- ing cells on the slide, and digitally store color images of each cell scene. The images are then viewed on the review station by a cytologist who triages the smear as either ‘negative’, or ‘review,’ in- dicating the need for further microscopic examination. Fig. 1 (a and b) depict the scanning and review processes.

It is through the use of neural network technology that this system can analyze the conventional cervical smear, with its wide variations in preparation, staining, overlapping cells and morphology. Since neural networks are so well suited for complex pattern recognition, they hold promise for use in other applications in cytopathology and histo- pathology.

2. Materials and methods

2.1. The PA PNET scanner

The scanner includes a robotic arm for loading

L.J. Mango/Cancer Letr. 77 (1994) 155-162 157

and unloading slides from a storage container, an automated microscope and color camera for imaging the slide, a high speed image processor and neural network accelerator for analysis, and an 80486 computer running MS-DOS for operator interface and system control. A bar-code reader is included to ensure proper slide identification. The automated microscope has three objectives (50 x , 200x and 400x magnification) which are used during the different phases of scanning. The scanner typically operates with minimal operator inter- vention while it processes a batch of up to 100 slides.

The scanning process takes less than 10 min/ smear, depending on cellularity and smear preparation. The process includes three passes across the slide. The first, low-power scan locates and maps the distribution of cellular material on the slide. This generates an estimate of slide cellularity and optimizes focusing.

During the medium-power scan, the algorithmic image processor locates the set of potentially abnormal cells using color processing and mathematical morphology (‘well algorithm’) to locate individual cells and clusters [7]. Single-cell and cell-cluster images are processed independently by two separate neural networks to produce a numerical ‘score’ based on the object’s resemblance to a training library of abnormal cells.

Both neural networks use a feed-forward, backpropagation architecture, as described elsewhere [1,9,26]. These networks consist of many simple processors (‘neurons’) arranged in layers. The input of each processor is connected to the output of each processor in the preceding layer through a unique and variable ‘synaptic weighting function’. During training, the weights are adjusted (back propagated) iteratively, until the desired output function from the entire network is achieved. The output is a ‘similarity score’ which is used for categorization. Thus, ‘learning’ occurs by adjust- ing the weights. This process, learning by example, is conceptually similar to that used by human experts.

The neural networks are trained with libraries containing large numbers of both positive (abnormal) and negative (normal) cells. Through train-

ing, the network learns to generalize from the specific examples used. Once properly trained, the weights remain fixed and the neural network can correctly categorize new objects. The advantage of this approach is that it is not limited by a set of fixed rules, so that it can tolerate highly variable inputs, such as overlapping and diversely shaped cells, while providing robust pattern recognition capabilities.

Following the medium-power classification scan, a final re-scan is performed. High-resolution color images of the 64 highest-ranked objects from each of the two neural networks (totaling 128 cellular fields) are captured for storage on digital tape, with the slide coordinates of each object recorded.

It should be noted that the scanner does not perform a ‘diagnosis,’ since its objective is to automate the search for abnormality. Rather, it finds and records 128 of the most abnormal looking scenes on the slide, leaving the ultimate diagnosis to a cytologist. In ‘positive’ cases, potentially diagnostic cells (and usually some other abnormal looking objects) are recorded. In ‘negative’ cases, the 128 objects are simply the most abnormal looking scenes on the slide.

2.2. The PAPNET review station The review station is located in the cytology lab-

oratory where manual screening normally takes place. It is used by cytologists to view the 128 images from each slide which the scanner had selected and recorded. The review station includes a PC computer with a mouse and keyboard for control, a digital tape drive for storing images, and a high-resolution color monitor for image display. One of the laboratory’s existing microscopes is used by a cytologist to manually examine slides when potentially diagnostic cells are displayed on the review station.

In typical use, a cytotechnologist first retrieves the digital images of each slide from the tape. For each slide, two separate pages of images are displayed on the review station’s monitor. The first page shows 64 scenes of single cells, while the sec- ond page shows 64 cell-cluster scenes. Each image can be magnified for closer examination and

158 L.J. Mango / Cancer Lerr. 77 (1994) 155-162

marked if it appears to be diagnostic. During review, cytotechnologists use their interpretive skills to triage each case as either ‘negative’ (within normal limits, requiring no microscopic analysis), ‘inadequate’ (requiring further microscopic analysis because of insufficient cellular information), or ‘review’ (potentially suspicious cells found requir-

ing microscopic analysis for final diagnosis). Typically, evaluation and triage of a smear take approximately 1 min on the review station. For microscopic analysis, the cytotechnologist can readily find the suspected cell by using its slide coordinates, as displayed on the PAPNET review station. If true abnormalities are detected with the

Fig. I, (a) Scanning: diagram depictmg transfer of cervtcal smears to PAPNET scanning center. scanning on automated microscope

using image processing and neural network computers, and return with digital tape recording most suspicious scenes from each slide.

(b) Review: pictorial representation of PAPNET review station in the cytology laboratory. with mouse and high-resolution color

monitor for cell scene display. The cytologist interprets scenes from each smear to determine whether microscopic review is required.

Fig. 2. PAPNET image drsplay of cells selected by the neural network from a smear falsely classifted as negative, as a result of routine

manual screening. This patient was diagnosed with metastatic cervical carcinoma 2 years after this false negative smear. The yellow

bars shown in one tile display the scene’s slide location in X and Y coordinates. Several highly abnormal, very small cells are

recognizable on the display, seen centered and magnified (400 x ) in the tiles marked with green ‘M’s’

L.J. Mango/Cancer Lett. 77 (1994) 155-162 159

microscope, the smear is ultimately referred to a cytopathologist for final diagnosis.

2.3. Study methodology In all of the studies presented here, the

PAPNET system was used to scan archived, conventionally prepared cervical smears. All par- ticipants were unaware of the smears’ original diagnoses during PAPNET review. In addition, the users were all trained in the PAPNET-assisted screening methodology [23]. This included familiarization with the review station and its operation, training in the recognition of normal and various types of abnormal cells as displayed on the monitor, as well as understanding the study protocol. In each study, the system’s sensitivity to specific abnormalities present on the smears was computed, as were triage efficiency values indicat- ing the specificity of the reviewer’s triage results. Some studies involved a final phase of microscopic re-examination of discrepant smears, i.e., false negatives and false positives from either methodology. Sensitivities, specificities and predictive values were recalculated when appropriate.

3. Results

Neural network-selected cells from each smear studied were displayed on a high-resolution color video monitor for review by trained cytologists. These potentially abnormal cells are centered, magnified at 200x and displayed within the sur- rounding 120 pm* of background. Sixteen of the 128 cell scenes selected by the system from a false- negative smear are shown in Fig. 2 (bottom, centre).

In a study involving the Instituto Nazionale per lo Studio e la Cura dei Tumori in Milan, Italy [21], PAPNET sensitivity was assessed by scanning a collection of 200 archived cervical smears, which comprised 150 consecutive negative smears and 50 arbitrarily selected smears, representing a variety of abnormalities and diagnostic findings, including atypias, CIN Is, 11s and 111s squamous cell carcinomas, adenocarcinomas, endometrial cells, chlamydia, koilocytosis, and reactive changes. During PAPNET review, triage classification and

preliminary diagnosis were made for each smear by two cytopathologists, based on the images displayed by PAPNET. All 33 cases which had been previously classified by manual screening as abnormal, ranging from atypias to adenocar- cinemas, were correctly triaged as ‘review’ by PAPNET-assisted screening. Overall sensitivity to the full range of cytologic findings and abnormalities, including endometrial cell presence, reactive changes and chlamydia, was calculated to be 96%. The reviewers’ triage eficiency, including the ‘inadequate’ smears, was 8 1%.

The cytology laboratory at Hinsdale Hospital in Illinois participated in a study designed to determine the efficiency of the system and to evaluate its potential for use in rescreening [ 121. This inves- tigation included 19 1 archived, manually screened cervical smears: 72 were initially diagnosed as negative, seven were inadequate for diagnosis, and the remaining 112 smears were positive cases, with abnormalities including a variety of disease states. Following PAPNET scanning, each case was independently reviewed and triaged by two cytologists. After comparison of the initial manual screening results with those obtained using PAPNET-assisted screening, the discrepant smears were microscopically re-evaluated, using the supplied coordinates of cell location, and final diagnoses were imparted. The system was able to detect two abnormal cases, a condyloma and an atypia, which had been missed by manual screening. PAPNET-assisted screening failed to detect one CIN I case which was manually diagnosed. None of the positive cases were missed by both screening methods. The system’s sensitivity was shown to be 98”/0 for each cytologist’s review. Triage efficiency values for the reviewers were calculated to be 93”/0 and 97%.

In another study aimed at evaluating its sensitivity and efficiency, the system was used to screen cervical smears which had been obtained from 220 consecutive patients at University of Iowa Hospi- tals and Clinics [25]. Triage results indicated that 155 of the cases were negative, and 53 needed further microscopic analysis; 12 were inadequate for interpretation. Of the 155 cases triaged as ‘negative,’ 153 were originally diagnosed as such; the other 2 were diagnosed as abnormal. Of the 53

160 L.J. Mango/Cancer Lets. 77 (1994) 155-162

cases that were triaged as ‘review’ with the system, 18 were initially diagnosed as abnormal by manual screening; the remaining 35 cases had been initially diagnosed as negative. Based on the 208 cases that were adequate for interpretation in this study, the calculated values for the system’s sensitivity to abnormalities and triage efficiency were found to be 90% and 81%, respectively. Blinded, microscopic re-evaluation of the 37 discrepant cases resulted in the reclassification of two false negatives and 10 false positives. Therefore, the system’s sensitivity increased to 100% with a triage effkiency of 85”/0. Based on the PAPNET system’s detection of 10 false-negative cases, the sensitivity of manual screening in this study was 71%.

than 50% in the false-negative rate when the PAPNET system was used. Correlations were made, with respect to sensitivity and triage efficiency, between both manual- and PAPNET- assisted screening. These results, together with those from the other studies described herein, are summarized in Table 1.

In a recent study performed at Ohio State Uni- versity [ 111, this adjunctive screening system was used to evaluate 357 smears from patients having had biopsies either at the time of cytology or within the following year. Biopsy results indicated that 211 (59%) patients had abnormal and 146 (41%) had normal histologies. The results of PAPNET-assisted screening were compared to those of manual screening: 73% of all smears were triaged as ‘review’ during PAPNET-assisted screening, compared to 63% manually diagnosed as abnormal. When these results were compared to the biopsy findings for the cases, false-negative rates were calculated for both PAPNET-assisted screening and manual screening to be 3.08% and 6.44%, respectively, showing a reduction of more

The PAPNET system may be particularly useful in rescreening cervical smears because of its sensitivity to the types of cytologic abnormalities typically missed by manual screening. In a recently published study from the Netherlands [4], the PAPNET system was shown to detect cancer cells in smears that were repeatedly misdiagnosed during manual screening. Ten archived false-negative smears from patients with histologically proven invasive cervical carcinoma were selected for rescreening by two pathologists; 10 smears, which were true-positive for invasive carcinoma, were used as controls in addition to 10 true-negative smears. During PAPNET-assisted rescreening, all false-negative cases were detected by the system, displaying abnormal cells or abnormal epithelial fragments, and all of the smears studied were subsequently reclassified by both pathologists as suspicious or carcinomas.

4. Discussion

While the conventionally prepared and manually screened cervical smear has been credited with

Table 1 Calculated values for sensitivity to abnormalities and triage efficiency in PAPNET-assisted screening of cervical smears

Investigator Cases Manual

(n] screening

Sensitivity

(‘X)

Rilke et al. [21] 200 n/a

Kish et al. [12]: observer No. I 191 91

observer No. 2

Slagel et al. [25]: initial screen 208 71

re-review

Kharazi et al. [I lid 351 89

“Comparisons made with respect to biopsy findings.

Papnet-assisted screening

Sensitivity Triage

(‘X) efficiency

(“4)

96 81

9x 93

98 91

90 81

100 85

95 58

Positive Negative

predictive predictive

value (‘VI)) value (‘%I)

63 98

87 99

94 99

34 99

53 100

77 89

L.J. Mango/Cancer Lett. 77 (1994) 155-162

significantly contributing to the decrease in morbidity and mortality associated with cervical cancer, the high number of false negatives is not an acceptable consequence. Extensive, manual rescreening of cervical smears is not feasible due to current shortages of cytotechnologists worldwide. Furthermore, in developing countries, where cervical carcinoma is often the leading cause of oncologic mortality, sufficient numbers of trained cytotechnologists do not exist for mass cervical smear screening programs. For these reasons, there is a definite clinical need for automation of cervical smear screening.

The PAPNET Cytological Screening System is being presented as an efficient, sensitive tool for the adjunctive screening of gynecologic cytologic smears. This system employs neural network technology to enable computer analysis of the complex and variable conventional cervical smear. Neural networks, as adaptive, non-algorithmic artificial intelligence systems, have been recently applied, with markedly increased frequency, to pattern recognition problems in industry and medicine.

The studies presented demonstrate a mean sensitivity of 97% and negative predictive value of 96”% for this neural network screening system. It is most clinically relevant that a proposed adjunctive screening tool, like the PAPNET system, maximize the sensitivity and negative predictive value of the cervical cytologic examination.

The mean triage efficiency of 78% and positive predictive value of 72% reflect the reviewers’ tendency to ‘over-consult’ the microscope for con- firmation. These statistics, then, pertain mainly to workload issues. Since the cytologists participat- ing in these investigations were using the system for the first time, these values may be considered worst-case estimates. Triage efficiency of PAP- NET review tends to increase with the cytologist’s experience using the system [23].

Given its high sensitivity to common abnormalities and its ability to greatly increase the number of smears that can be analyzed, this system may be an effective primary screener for clinical laboratories or government screening programs.

The use of this system as a practical tool in routine rescreening may effectively diminish the number of false-negative smears by detecting

161

human errors. Investigational results indicate that PAPNET-assisted rescreening is sensitive to smears incorrectly reported as negative.

The characteristics of manual false-negative smears have been examined as part of the system’s ongoing rescreening investigations. Preliminary findings suggest a cytopathologic pattern in those false negatives that precede high grade cervical le- sions. Most often, abnormal cells detected by the neural network in these cases are of extremely small size, on the order of lymphocytes or histiocytes, and may show an angular or spindled morphology. Their minute size might explain how these cells had gone undetected during manual screening.

On the other hand, the smears comprising PAPNET false negatives are generally detected by conventional, manual screening. Thus, the com- bination of conventional, manual microscopic screening and PAPNET-assisted rescreening can yield a drastically reduced false-negative rate.

The results presented here suggest that this neural network-based, interactive instrument may serve to enhance the sensitivity of the conventional cervical smear as a screen for the early detection of cervical carcinoma and its precursors.

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Computer-assisted cervical cancer screening using neural networks