Importance of nuclear morphology in breast cancer prognosis.

The purpose of this study is to define prognostic relationships between computer-derived nuclear morphological features, lymph node status, and tumor size in breast cancer. Computer-derived nuclear size, shape, and texture features were determined in fine-needle aspirates obtained at the time of diagnosis from 253 consecutive patients with invasive breast cancer. Tumor size and lymph node status were determined at the time of surgery. Median follow-up time was 61.5 months for patients without distant recurrence. In univariate analysis, tumor size, nuclear features, and the number of metastatic nodes were of decreasing significance for distant disease-free survival. Nuclear features, tumor size, and the number of metastatic nodes were of decreasing significance for overall survival. In multivariate analysis, the morphological size feature, largest perimeter, was more predictive of disease-free and overall survival than were either tumor size or the number of axillary lymph node metastases. This morphological feature, when combined with tumor size, identified more patients at both the good and poor ends of the prognostic spectrum than did the combination of tumor size and axillary lymph node status. Our data indicate that computer analysis of nuclear features has the potential to replace axillary lymph node status for staging of breast cancer. If confirmed by others, axillary dissection for breast cancer staging, estimating prognosis, and selecting patients for adjunctive therapy could be eliminated.

Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosis.

BACKGROUND: Both axillary lymph node involvement and tumor anaplasia, as expressed by visually assessed grade, have been shown to be prognostically important in breast carcinoma outcome. In this study, axillary lymph node involvement was used as the standard against which prognostic estimations based on computer-derived nuclear features were gauged. METHODS: The prognostic significance of nuclear morphometric features determined by computer-based image analysis were analyzed in 198 consecutive preoperative samples obtained by fine-needle aspiration (FNA) from patients with invasive breast carcinoma. A novel multivariate prediction method was used to model the time of distant recurrence as a function of the nuclear features. Prognostic predictions based on the nuclear feature data were cross-validated to avoid overly optimistic conclusions. The estimated accuracy of these prognostic determinations was compared with determinations based on the extent of axillary lymph node involvement. RESULTS: The predicted outcomes based on nuclear features were divided into three groups representing best, intermediate, and worst prognosis, and compared with the traditional TNM lymph node stratification. Nuclear feature stratification better separated the prognostically best from the intermediate group whereas lymph node stratification better separated the prognostically intermediate from the worst group. Prognostic accuracy was not increased by adding lymph node status or tumor size to the nuclear features. CONCLUSIONS: Computer analysis of a preoperative FNA more accurately identified prognostically favorable patients than did pathologic examination of axillary lymph nodes and may obviate the need for routine axillary lymph node dissection.

Indeterminate fine-needle aspiration of the breast. Image analysis-assisted diagnosis.

BACKGROUND: Fine-needle aspiration (FNA) of the breast, although effective for the diagnosis of breast carcinoma, has a significant drawback. A minority of cases cannot be classified as benign or malignant. These FNAs are assigned an inconclusive diagnosis, often prompting surgical biopsy. Surgery is justified in some of these cases, but many of these lesions are benign. If these inconclusive FNAs could be accurately diagnosed as benign or malignant, many of these patients might avoid having to undergo surgical biopsy. METHODS: An image analysis and an automated learning system that was developed at the University of Wisconsin (Xcyt) was used to categorize 56 (37 benign and 19 malignant) breast FNAs diagnosed as "indeterminate" and the computer diagnosis compared with the surgical biopsy. For each case, an operator chose a group of cells within a single field on the FNA slide and digitized this image using a video camera. The outline of each nucleus was manually outlined, and the exact border was delineated by the computer. Based on the analysis of three nuclear features (area, texture, and smoothness), the Xcyt system computed a benign or malignant diagnosis and a corresponding probability of malignancy for each case. RESULTS: Probabilities of malignancy for the respective cases ranged from 0.0-1.0. Benign cases were defined as those having probabilities of malignancy < 0.3; those with probabilities above this limit were considered malignant. Using these criteria, the computer identified 33 cases as benign and 23 cases as malignant. When compared with the surgical biopsy, 42 of the cases (75%) were correctly classified with a sensitivity and specificity of 73.7% and 75.7%, respectively. There were only 5 false-negative cases with a false-negative rate of 13.5% and a predictive value of a negative test of 84.8%. CONCLUSIONS: When faced with inconclusive diagnoses of FNAs of breast masses, the authors believe that image analysis may be used as an aid in the further classification of such lesions, thereby providing a more appropriate triage for surgical biopsy.

Computer-derived nuclear "grade" and breast cancer prognosis.

Visual assessments of nuclear grade are subjective yet still prognostically important. Now, computer-based analytical techniques can objectively and accurately measure size, shape and texture features, which constitute nuclear grade. The cell samples used in this study were obtained by fine needle aspiration (FNA) during the diagnosis of 187 consecutive patients with invasive breast cancer. Regions of FNA preparations to be analyzed were digitized and displayed on a computer monitor. Nuclei to be analyzed were roughly outlined by an operator using a mouse. Next, the computer generated a "snake" that precisely enclosed each designated nucleus. Ten nuclear features were then calculated for each nucleus based on these snakes. These results were analyzed statistically and by an inductive machine learning technique that we developed and call "recurrence surface approximation" (RSA). Both the statistical and RSA machine learning analyses demonstrated that computer-derived nuclear features are prognostically more important than are the classic prognostic features, tumor size and lymph node status.

Computer-derived nuclear features distinguish malignant from benign breast cytology.

This article describes the use of computer-based analytical techniques to define nuclear size, shape, and texture features. These features are then used to distinguish between benign and malignant breast cytology. The benign and malignant cell samples used in this study were obtained by fine needle aspiration (FNA) from a consecutive series of 569 patients: 212 with cancer and 357 with fibrocystic breast masses. Regions of FNA preparations to be analyzed were converted by a video camera to computer files that were displayed on a computer monitor. Nuclei to be analyzed were roughly outlined by an operator using a mouse. Next, the computer generated a "snake" that precisely enclosed each designated nucleus. The computer calculated 10 features for each nucleus. The ability to correctly classify samples as benign or malignant on the basis of these features was determined by inductive machine learning and logistic regression. Cross-validation was used to test the validity of the predicted diagnosis. The logistic regression cross validated classification accuracy was 96.2% and the inductive machine learning cross-validated classification accuracy was 97.5%. Our computerized system provides a probability that a sample is malignant. Should this probability fall between 30% and 70%, the sample is considered "suspicious," in the same way a visually graded FNA may be termed suspicious. All of the 128 consecutive cases obtained since the introduction of this system were correctly diagnosed, but nine benign aspirates fell into the suspicious category.(ABSTRACT TRUNCATED AT 250 WORDS)

Computerized breast cancer diagnosis and prognosis from fine-needle aspirates.

OBJECTIVE: To use digital image analysis and machine learning to (1) improve breast mass diagnosis based on fine-needle aspirates and (2) improve breast cancer prognostic estimations. DESIGN: An interactive computer system evaluates, diagnoses, and determines prognosis based on cytologic features derived from a digital scan of fine-needle aspirate slides. SETTING: The University of Wisconsin (Madison) Departments of Computer Science and Surgery and the University of Wisconsin Hospital and Clinics. PATIENTS: Five hundred sixty-nine consecutive patients (212 with cancer and 357 with benign masses) provided the data for the diagnostic algorithm, and an additional 118 (31 with malignant masses and 87 with benign masses) consecutive, new patients tested the algorithm. One hundred ninety of these patients with invasive cancer and without distant metastases were used for prognosis. INTERVENTIONS: Surgical biopsy specimens were taken from all cancers and some benign masses. The remaining cytologically benign masses were followed up for a year and surgical biopsy specimens were taken if they changed in size or character. Patients with cancer received standard treatment. OUTCOME MEASURES: Cross validation was used to project the accuracy of the diagnostic algorithm and to determine the importance of prognostic features. In addition, the mean errors were calculated between the actual times of distant disease occurrence and the times predicted using various prognostic features. Statistical analyses were also done. RESULTS: The predicted diagnostic accuracy was 97% and the actual diagnostic accuracy on 118 new samples was 100%. Tumor size and lymph node status were weak prognosticators compared with nuclear features, in particular those measuring nuclear size. Compared with the actual time for recurrence, the mean error of predicted times for recurrence with the nuclear features was 17.9 months and was 20.1 months with tumor size and lymph node status (P = .11). CONCLUSION: Computer technology will improve breast fine-needle aspirate accuracy and prognostic estimations.

Image analysis and machine learning applied to breast cancer diagnosis and prognosis.

Fine needle aspiration (FNA) accuracy is limited by, among other factors, the subjective interpretation of the aspirate. We have increased breast FNA accuracy by coupling digital image analysis methods with machine learning techniques. Additionally, our mathematical approach captures nuclear features ("grade") that are prognostically more accurate than are estimates based on tumor size and lymph node status. An interactive computer system evaluates, diagnoses and determines prognosis based on nuclear features derived directly from a digital scan of FNA slides. A consecutive series of 569 patients provided the data for the diagnostic study. A 166-patient subset provided the data for the prognostic study. An additional 75 consecutive, new patients provided samples to test the diagnostic system. The projected prospective accuracy of the diagnostic system was estimated to be 97% by 10-fold cross-validation, and the actual accuracy on 75 new samples was 100%. The projected prospective accuracy of the prognostic system was estimated to be 86% by leave-one-out testing.

Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates.

An interactive computer system evaluates and diagnoses based on cytologic features derived directly from a digital scan of fine-needle aspirate (FNA) slides. A consecutive series of 569 patients provided the data to develop the system and an additional 54 consecutive, new patients provided samples to test the system. The projected prospective accuracy of the system estimated by tenfold cross validation was 97%. The actual accuracy on 54 new samples (36 benign, 1 atypia, and 17 malignant) was 100%. Digital image analysis coupled with machine learning techniques will improve diagnostic accuracy of breast fine needle aspirates.

Breast cytology diagnosis with digital image analysis.

An interactive computer system has been developed for evaluating cytologic features derived directly from a digital scan of breast fine needle aspirate slides. The system uses computer vision techniques to analyze cell nuclei and classifies them using an inductive method based on linear programming. A digital scan of selected areas of the aspirate slide is done by a trained observer, while the analysis of the digitized image is done by an untrained observer. When trained and tested on 119 breast fine needle aspirates (68 benign and 51 malignant) using leave-one-out testing, 90% correctness was achieved. These results indicate that the method is accurate (good intraobserver and interobserver reproducibility) and that an untrained operator can obtain diagnostic results comparable to those achieved visually by experienced observers.

The effects of intermittent progesterone upon tamoxifen inhibition of tumor growth in the 7,12-dimethylbenzanthracene rat mammary tumor model.

The development of endometrial cancer is a potential risk during long-term tamoxifen therapy for breast cancer. In order to protect the uterus, progestin treatment has been proposed for these patients. However, within the 7,12-dimethylbenzanthracene-induced rat mammary model, progesterone is known to reverse the antitumor effects of tamoxifen. This study shows that progesterone administered intermittently still reverses the antitumor effects of tamoxifen in this model. This effect of progesterone is not due to a decrease in the tissue levels of tamoxifen, and may be direct, via the progesterone receptor.

Computer-designed expert systems for breast cytology diagnosis.

Three expert systems have been developed to diagnose from nine scalar values visually assigned to epithelial cells obtained by breast fine needle aspiration. These expert systems achieved up to 0.98 sensitivity and 0.97 specificity. When applied to 804 breast masses, the clinical sensitivity was 0.98 and specificity was 0.93 (exclusive of the 0.04 unsatisfactory aspirates). Cancers can be missed physically during the aspiration process; thus, some clinically suspicious masses were biopsied despite benign cytology. This contributed to the difference between the expert system and clinical specificities.

Surgical options in 424 patients with primary breast cancer without systemic metastases.

Mastectomy was dictated by medical considerations in about 40% of 424 consecutive patients who had invasive breast cancer with no evidence of systemic metastases. Half of the women considered suitable for local excision of the breast cancer followed by radiotherapy (conservation) elected to have mastectomy, and intraoperative findings dictated mastectomy in about 20% of those electing conservation. Consequently, conservation was accomplished in about one quarter of those treated for breast cancer. Younger rather than older women more frequently expressed the desire for breast conservation.

Multisurface method of pattern separation for medical diagnosis applied to breast cytology.

Multisurface pattern separation is a mathematical method for distinguishing between elements of two pattern sets. Each element of the pattern sets is comprised of various scalar observations. In this paper, we use the diagnosis of breast cytology to demonstrate the applicability of this method to medical diagnosis and decision making. Each of 11 cytological characteristics of breast fine-needle aspirates reported to differ between benign and malignant samples was graded 1 to 10 at the time of sample collection. Nine characteristics were found to differ significantly between benign and malignant samples. Mathematically, these values for each sample were represented by a point in a nine-dimensional space of real variables. Benign points were separated from malignant ones by planes determined by linear programming. Correct separation was accomplished in 369 of 370 samples (201 benign and 169 malignant). In the one misclassified malignant case, the fine-needle aspirate cytology was so definitely benign and the cytology of the excised cancer so definitely malignant that we believe the tumor was missed on aspiration. Our mathematical method is applicable to other medical diagnostic and decision-making problems.

Mastectomy or breast conservation in the management of primary breast cancer: psychosocial factors.

A diagnosis of breast cancer causes significant emotional, social, and vocational upheaval which diminishes over time. However, newly diagnosed breast cancer patients are mentally capable of making treatment decisions and should be offered the available treatment options. A significant proportion of patients who have the option of breast conservation will choose mastectomy instead. Saving the breast does not significantly alter adjustment problems.

Computer-aided diagnosis of breast aspirates via expert systems.

Two computer-driven expert systems trained to correctly diagnose 369 fine needle aspirates of the breast on the basis of nine cytologic descriptive parameters were tested on 70 newly obtained aspirates (57 benign and 13 malignant). The system generated by multisurface pattern separation misclassified one malignant test sample (i.e., one false negative) while the system generated by a connectionist algorithm (neural network) misclassified two benign test samples (i.e., two false positives). A decision tree misclassified three of the benign test samples (i.e., three false positives). These expert systems aid in the cytologic diagnosis of breast aspirates and can serve as models for other applications.

Adjuvant trial of 12 cycles of CMFPT followed by observation or continuous tamoxifen versus four cycles of CMFPT in postmenopausal women with breast cancer: an Eastern Cooperative Oncology Group phase III study.

A prospectively controlled randomized trial to compare the adjuvant efficacy of 12 cycles of cyclophosphamide, methotrexate, fluorouracil, prednisone, and tamoxifen (CMFPT) followed by observation or a total of 5 years of continuous tamoxifen versus four cycles of CMFPT followed by observation in postmenopausal women with operable node-positive breast cancer was started by the Eastern Cooperative Oncology Group (ECOG) in 1982. In 1986 the study was modified to allow patients on CMFPT X 12 plus continuous tamoxifen to be rerandomized after completing 5 years of tamoxifen to either continue for life or to stop therapy. Patients were stratified for number of involved nodes and estrogen-receptor (ER) status and randomized to receive one of three treatments: CMFPT X 4, CMFPT X 12, or CMFPT X 12 plus continuous tamoxifen. Of 962 patients entered on the study, 803 were eligible. Life-threatening toxicity occurred in 75 and lethal toxicity in seven patients. Median follow-up is 4.1 years; 279 patients had recurrent disease. Time to relapse (TTR) is significantly longer for patients on CMFPT X 12 plus continuous tamoxifen than for CMFPT X 4 (P = .002), or CMFPT X 12 (P = 0.05). Differences between four or 12 cycles of CMFPT are not significant; relapse-free rates at 5 years are 61% for CMFPT X 12 plus continuous tamoxifen, 51% on CMFPT X 12, and 52% on CMFPT X 4. Treatment differences in overall survival are not significant. Hormone receptor status and number of involved nodes were found to be significant prognostic parameters.

Factors women take into account when deciding upon type of surgery for breast cancer.

For women with stage I or II breast cancer, randomized trials have demonstrated no significant difference in survival rates between women receiving modified radical mastectomy (MRM) and women receiving breast conserving (BC) surgery. Therefore, many women are now in a position of having a choice between these two options. Twenty-two women who met the surgical criteria for having this choice were interviewed 1 to 2 weeks postsurgery to determine factors they had considered when deciding between MRM and BC, how much they wished to participate in decision-making, and the sources of information they used. The sample was purposefully limited to women attending one clinic in order to insure control over variables such as the information to which patients are exposed. When asked why they had chosen a given surgery, two factors, concerns about radiotherapy (p = 0.003) and body integrity (p = 0.04), emerged as significantly different for women choosing BC vs. MRM. Furthermore, women reported that participation in decision-making was important to them and that they had had sufficient participation in the decision-making process. Finally, they rated "people" sources of information as more important than written or visual materials, suggesting that nurses and other care providers are important in supporting women through the decision-making process.

Cosmesis and local control after irradiation in women treated conservatively for breast cancer.

One hundred twenty-two patients (124 tumor sites) with breast carcinoma (T1 to T3, N0 to N2, M0) were treated by a lumpectomy and radiation therapy at the University of Wisconsin, Madison, from June 1978 to December 1986. Irradiation to the breast and regional lymph nodes was carried out with cobalt 60 teletherapy in 2-Gy fractions to 50 Gy, followed by an additional boost of 10 Gy to the tumorectomy site with coned electrons. Cosmesis was analyzed by scoring the effects of surgery, as well as the effects of irradiation, and by photographic assessment. After a follow-up of 24 to 119 months (median, 36 months), 82% of the patients were found to have good or excellent cosmetic scores. There was a trend that favored better cosmetic results in younger patients and in patients with outer-quadrant lesions. No significant impact of adjuvant chemotherapy on the cosmetic outcome or on the complications of treatment was demonstrated. There were six local recurrences (5.2%), three of which were true recurrences and the other three that were new primary tumors; 11 distant failures (9%) occurred. The following side effects from irradiation developed in only 5 patients (4.1%): match-line fibrosis (n = 2), soft-tissue necrosis (n = 1), and persistent tenderness (n = 2). The conditions of the two patients with persistent tenderness responded favorably with conservative management. We concluded that a lumpectomy, followed by radiation therapy, provides good cosmetic results without compromising the local control rate. If adjuvant chemotherapy is planned, we recommend that it be administered before radiation therapy in favor of concomitant therapy with both modalities.