How cancer at the primary site and in the lymph nodes contributes to the risk of cancer death

Population-based prognostic instrument (SweMR 2.0) for melanoma-specific survival - An ideal tool for individualised treatment decisions for Swedish patients

INTRODUCTION

The prognosis for patients with melanoma has improved due to better treatments in recent years and updated tools to accurately predict an individual's risk are warranted. This study aims to describe a prognostic instrument for patients with cutaneous melanoma and its potential as a clinical device for treatment decisions.

METHODS

Patients with localised invasive cutaneous melanoma diagnosed in 1990-2021 with data on tumour thickness were identified from the population-based Swedish Melanoma Registry. The parametric Royston-Parmar (RP) method was used to estimate melanoma-specific survival (MSS) probabilities. Separate models were constructed for patients (≤1 mm) and (>1 mm) and prognostic groups were created based on all combinations of age, sex, tumour site, tumour thickness, absence/presence of ulceration, histopathologic type, Clark's level of invasion, mitoses and sentinel lymph node (SLN) status.

RESULTS

In total, 72 616 patients were identified, 41 764 with melanoma ≤1 mm and 30 852 with melanoma >1 mm. The most important variable was tumour thickness for both (≤1 mm) and (>1 mm), that explained more than 50% of the survival. The second most important variables were mitoses (≤1 mm) and SLN status (>1 mm). The prognostic instrument successfully created probabilities for >30 000 prognostic groups.

CONCLUSIONS

The Swedish updated population-based prognostic instrument, predicts MSS survival up to 10 years after diagnosis. The prognostic instrument gives more representative and up-to-date prognostic information for Swedish patients with primary melanoma than the present AJCC staging. Additional to clinical use and the adjuvant setting, the information retrieved could be used to plan future studies.

Prospective comparison of CT and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients: Initial results

Objectives

To compare the diagnostic accuracy of contrast-enhanced thoraco-abdominal computed tomography and whole-body ¹⁸F-FDG PET/MRI in N and M staging in newly diagnosed, histopathological proven breast cancer.

Material and methods

A total of 80 consecutive women with newly diagnosed and histopathologically confirmed breast cancer were enrolled in this prospective study. Following inclusion criteria had to be fulfilled: (1) newly diagnosed, treatment-naive T2-tumor or higher T-stage or (2) newly diagnosed, treatment-naive triple-negative tumor of every size or (3) newly diagnosed, treatment-naive tumor with molecular high risk (T1c, Ki67 >14%, HER2neu over-expression, G3). All patients underwent a thoraco-abdominal ceCT and a whole-body ¹⁸F-FDG PET/MRI. All datasets were evaluated by two experienced radiologists in hybrid imaging regarding suspect lesion count, localization, categorization and diagnostic confidence. Images were interpreted in random order with a reading gap of at least 4 weeks to avoid recognition bias. Histopathological results as well as follow-up imaging served as reference standard. Differences in staging accuracy were assessed using Mc Nemars chi² test.

Results

CT rated the N stage correctly in 64 of 80 (80%, 95% CI:70.0–87.3) patients with a sensitivity of 61.5% (CI:45.9–75.1), a specificity of 97.6% (CI:87.4–99.6), a PPV of 96% (CI:80.5–99.3), and a NPV of 72.7% (CI:59.8–82.7). Compared to this, ¹⁸F-FDG PET/MRI determined the N stage correctly in 71 of 80 (88.75%, CI:80.0–94.0) patients with a sensitivity of 82.1% (CI:67.3–91.0), a specificity of 95.1% (CI:83.9–98.7), a PPV of 94.1% (CI:80.9–98.4) and a NPV of 84.8% (CI:71.8–92.4). Differences in sensitivities were statistically significant (difference 20.6%, CI:-0.02–40.9; p = 0.008). Distant metastases were present in 7/80 patients (8.75%). ¹⁸ F-FDG PET/MRI detected all of the histopathological proven metastases without any false-positive findings, while 3 patients with bone metastases were missed in CT (sensitivity 57.1%, specificity 95.9%). Additionally, CT presented false-positive findings in 3 patients.

Conclusion

¹⁸F-FDG PET/MRI has a high diagnostic potential and outperforms CT in assessing the N and M stage in patients with primary breast cancer.

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

Prospective evaluation of whole-body MRI and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients

OBJECTIVES

To evaluate and compare the diagnostic potential of whole-body MRI and whole-body 18F-FDG PET/MRI for N and M staging in newly diagnosed, histopathologically proven breast cancer.

MATERIAL AND METHODS

A total of 104 patients (age 53.4 ± 12.5) with newly diagnosed, histopathologically proven breast cancer were enrolled in this study prospectively. All patients underwent a whole-body 18F-FDG PET/MRI. MRI and 18F-FDG PET/MRI datasets were evaluated separately regarding lesion count, lesion localization, and lesion characterization (malignant/benign) as well as the diagnostic confidence (5-point ordinal scale, 1-5). The N and M stages were assessed according to the eighth edition of the American Joint Committee on Cancer staging manual in MRI datasets alone and in 18F-FDG PET/MRI datasets, respectively. In the majority of lesions histopathology served as the reference standard. The remaining lesions were followed-up by imaging and clinical examination. Separately for nodal-positive and nodal-negative women, a McNemar chi2 test was performed to compare sensitivity and specificity of the N and M stages between 18F-FDG PET/MRI and MRI. Differences in diagnostic confidence scores were assessed by Wilcoxon signed rank test.

RESULTS

MRI determined the N stage correctly in 78 of 104 (75%) patients with a sensitivity of 62.3% (95% CI: 0.48-0.75), a specificity of 88.2% (95% CI: 0.76-0.96), a PPV (positive predictive value) of 84.6% % (95% CI: 69.5-0.94), and a NPV (negative predictive value) of 69.2% (95% CI: 0.57-0.8). Corresponding results for 18F-FDG PET/MRI were 87/104 (83.7%), 75.5% (95% CI: 0.62-0.86), 92.2% (0.81-0.98), 90% (0.78-0.97), and 78.3% (0.66-0.88), showing a significantly better sensitivity of 18F-FDG PET/MRI determining malignant lymph nodes (p = 0.008). The M stage was identified correctly in MRI and 18F-FDG PET/MRI in 100 of 104 patients (96.2%). Both modalities correctly staged all 7 patients with distant metastases, leading to false-positive findings in 4 patients in each modality (3.8%). In a lesion-based analysis, 18F-FDG PET/MRI showed a significantly better performance in correctly determining malignant lesions (85.8% vs. 67.1%, difference 18.7% (95% CI: 0.13-0.26), p < 0.0001) and offered a superior diagnostic confidence compared with MRI alone (4.1 ± 0.7 vs. 3.4 ± 0.7, p < 0.0001).

CONCLUSION

18F-FDG PET/MRI has a better diagnostic accuracy for N staging in primary breast cancer patients and provides a significantly higher diagnostic confidence in lesion characterization than MRI alone. But both modalities bear the risk to overestimate the M stage.

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality

The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.

Impact of 18F-FDG PET/MR on therapeutic management in high risk primary breast cancer patients - A prospective evaluation of staging algorithms

PURPOSE

To investigate whether potential differences in staging between a traditional staging imaging algorithm and ¹⁸F-FDG PET/MR lead to a change in patient management in breast carcinoma and to compare the diagnostic accuracy between the traditional staging algorithm and ¹⁸F-FDG PET/MR for the TNM classification.

METHOD

In this prospective cohort study from two university hospitals 56 women with newly diagnosed, therapy-naive breast cancer and increased pre-test probability for distant metastases were included. All patients were examined by a traditional staging imaging algorithm (X-ray mammography, breast ultrasonography, chest plain radiography, bone scintigraphy, and ultrasonography of the liver and axillary fossa) and whole-body ¹⁸F-FDG PET/MR including dedicated ¹⁸F-FDG PET/MR breast examinations. Each patient was discussed two times in a separate tumor board session to determine a total of three therapy recommendations based on histopathological data of the primary tumor and (1) traditional algorithm only, (2) traditional algorithm and ¹⁸F-FDG PET/MR, and (3) ¹⁸F-FDG PET/MR only. Major changes in therapy recommendations and differences between the traditional staging algorithm and ¹⁸F-FDG PET/MR for the TNM classification were evaluated.

RESULTS

Staging by ¹⁸F-FDG PET/MR led to a difference in treatment compared the traditional staging algorithm in 8/56 cases (14%). Therapy changes included therapy of the breast, locoregional nodes and systemic therapy. A trend to staging superiority was found for ¹⁸F-FDG PET/MRI without statistical significance (p = 0.3827).

CONCLUSION

In conclusion, for breast cancer patients with elevated pre-test probability for distant metastases a change of the therapy regiment occurs in 14 % of patients when staged by ¹⁸F-FDG PET/MR and confirmed by histopathology compared to a traditional staging algorithm. In particular with regard to the amendment of the guideline further assessment of ¹⁸F-FDG-PET/MR in this setting is necessary to assess the true value of this modality.

Local and whole-body staging in patients with primary breast cancer: a comparison of one-step to two-step staging utilizing 18F-FDG-PET/MRI

OBJECTIVES

The purpose of this study was to compare the diagnostic value of a one-step to a two-step staging algorithm utilizing ¹⁸F-FDG PET/MRI in breast cancer patients.

METHODS

A total of 38 patients (37 females and one male, mean age 57 ± 10 years; range 31-78 years) with newly diagnosed, histopathologically proven breast cancer were prospectively enrolled in this trial. All PET/MRI examinations were assessed for local tumor burden and metastatic spread in two separate reading sessions: (1) One-step algorithm comprising supine whole-body ¹⁸F-FDG PET/MRI, and (2) Two-step algorithm comprising a dedicated prone ¹⁸F-FDG breast PET/MRI and supine whole-body ¹⁸F-FDG PET/MRI.

RESULTS

On a patient based analysis the two-step algorithm correctly identified 37 out of 38 patients with breast carcinoma (97%), while five patients were missed by the one-step ¹⁸F-FDG PET/MRI algorithm (33/38; 87% correct identification). On a lesion-based analysis 56 breast cancer lesions were detected in the two-step algorithm and 44 breast cancer lesions could be correctly identified in the one-step ¹⁸F-FDG PET/MRI (79%), resulting in statistically significant differences between the two algorithms (p = 0.0015). For axillary lymph node evaluation sensitivity, specificity and accuracy was 93%, 95 and 94%, respectively. Furthermore, distant metastases could be detected in seven patients in both algorithms.

CONCLUSION

The results demonstrate the necessity and superiority of a two-step ¹⁸F-FDG PET/MRI algorithm, comprising dedicated prone breast imaging and supine whole-body imaging, when compared to the one-step algorithm for local and whole-body staging in breast cancer patients.

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors

BACKGROUND

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease.

METHODS

To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index.

RESULTS

In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models.

CONCLUSIONS

This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Hyperlipidemia and hyper glycaemia in Breast Cancer Patients is related to disease stage

Objective:

The study was designed to determine the hyperlipidemia in breast cancer of patients at disease presentation, without any treatment and to correlate these variations with disease stage.

Methods:

This cross sectional study was conducted at Liaquat National teaching hospital in Karachi from 2006 to 2011, Age and family history of 208 breast cancer patients with infiltrating Ductal Carcinoma were compared with 176 matched control subjects. Married females were selected, with children and short breast feeding period. Cancer stage I-III was considered for the study and patients were grouped on the basis of Tumor grade, Tumor size, lymph node metastasis and disease free survival. Disease staging was based on tumor size and lymph node metastasis. Biochemical estimations included variations in random blood glucose level and lipid profile.

Results:

Lipid profile and random blood glucose level were found significantly high (p<0.05) compared to control subjects. Hyperlipidemia was significantly high in breast cancer patients with lymph node metastasis. On increase in tumor grade I to II, increase in total cholesterol (4%), LDL-cholesterol 23% and 11% increase in triglycerides was observed. On Tumor size increase from ≤2 to 2.5cm, increase observed in blood random glucose level was (4%), total cholesterol (1.7%) triglycerides (2%) and LDL (3%) whereas HDL was (2%) low. These variations remain insignificant on further increase in tumor size and grade.

Conclusion:

Study suggests that variation in lipid profile and blood random glucose level is associated with disease stage. No independent correlation of hyperlipidemia and hyperglycemia was developed with disease free survival.

Initial Clinical Findings of a Mathematical Model to Predict Survival of Head and Neck Cancer

Objectives

(1) Identify clinical features that impact survival for head and neck cancer. (2) Determine the individual contribution to mortality of significant clinical features. (3) Develop a web-based calculator to integrate clinical features and predict survival outcome for individual patients.

Study Design

Analysis of a national cancer database. We fit patient data to the binary-biological model of cancer lethality, a mathematical model designed to predict cancer outcome. The model predicts the risk of cancer death, using information on tumor size, nodal status, and other prognostic factors.

Subjects and Methods

Analysis was carried out on a cohort of ~50,000 patients with head and neck cancer from the Survey, Epidemiology and End-Results (SEER) 2009 data set and validated with a cohort of ~1300 patients from an institutional Massachusetts General Hospital/Massachusetts Eye and Ear Infirmary database. We developed a web-based calculator written in JavaScript, PHP, and HTML.

Results

The risk of death due to head and neck cancer increases monotonically with tumor size. Each positive lymph node is associated with ~14% extra risk of death. Anatomical site, age, race, tumor extension, N stage, and extracapsular spread contribute to mortality. The lethal impact of these prognostics factors can be accurately estimated by the Size + Nodes + PrognosticMarkers (SNAP) method.

Conclusions

This predictive cancer model and web-based calculator provide a basis for estimating the risk of death for head and neck cancer patients by assigning values to the lethal contributions of tumor size, number of positive nodes, anatomical site, tumor extension, N stage, extracapsular spread, age at diagnosis, and race.

The Oswestry Risk Index: an aid in the treatment of metastatic disease of the spine

The revised Tokuhashi, Tomita and modified Bauer scores are commonly used to make difficult decisions in the management of patients presenting with spinal metastases. A prospective cohort study of 199 consecutive patients presenting with spinal metastases, treated with either surgery and/or radiotherapy, was used to compare the three systems. Cox regression, Nagelkerke's R(2) and Harrell's concordance were used to compare the systems and find their best predictive items. The three systems were equally good in terms of overall prognostic performance. Their most predictive items were used to develop the Oswestry Spinal Risk Index (OSRI), which has a similar concordance, but a larger coefficient of determination than any of these three scores. A bootstrap procedure was used to internally validate this score and determine its prediction optimism. The OSRI is a simple summation of two elements: primary tumour pathology (PTP) and general condition (GC): OSRI = PTP + (2 - GC). This simple score can predict life expectancy accurately in patients presenting with spinal metastases. It will be helpful in making difficult clinical decisions without the delay of extensive investigations.

Are metastases from metastases clinical relevant? Computer modelling of cancer spread in a case of hepatocellular carcinoma

BACKGROUND

Metastasis formation remains an enigmatic process and one of the main questions recently asked is whether metastases are able to generate further metastases. Different models have been proposed to answer this question; however, their clinical significance remains unclear. Therefore a computer model was developed that permits comparison of the different models quantitatively with clinical data and that additionally predicts the outcome of treatment interventions.

METHODS

The computer model is based on discrete events simulation approach. On the basis of a case from an untreated patient with hepatocellular carcinoma and its multiple metastases in the liver, it was evaluated whether metastases are able to metastasise and in particular if late disseminated tumour cells are still capable to form metastases. Additionally, the resection of the primary tumour was simulated. The simulation results were compared with clinical data.

RESULTS

The simulation results reveal that the number of metastases varies significantly between scenarios where metastases metastasise and scenarios where they do not. In contrast, the total tumour mass is nearly unaffected by the two different modes of metastasis formation. Furthermore, the results provide evidence that metastasis formation is an early event and that late disseminated tumour cells are still capable of forming metastases. Simulations also allow estimating how the resection of the primary tumour delays the patient's death.

CONCLUSION

The simulation results indicate that for this particular case of a hepatocellular carcinoma late metastases, i.e., metastases from metastases, are irrelevant in terms of total tumour mass. Hence metastases seeded from metastases are clinically irrelevant in our model system. Only the first metastases seeded from the primary tumour contribute significantly to the tumour burden and thus cause the patient's death.

Prognosis of patients with breast cancer: causes of death and effects of time since diagnosis, age, and tumor characteristics

PURPOSE

The proportion of women living with a diagnosis of breast cancer in developed countries is increasing. Because breast cancer-specific deaths decrease with time since diagnosis, it is important to assess the burden of other causes of death in women diagnosed with breast cancer.

METHODS

Different causes of death within 10 years from diagnosis were assessed in 12,850 women younger than 75 years of age with stage 1 to 3 breast cancer diagnosed in Stockholm and Gotland regions 1990 to 2006. Flexible parametric survival models were used to estimate hazard ratios over time since diagnosis by tumor characteristics and age at diagnosis.

RESULTS

The proportion of deaths attributed to breast cancer ranged from 95.0% among women younger than age 45 years at diagnosis to 44.5% among women age 65 to 74 years. The proportions of circulatory system-specific deaths and deaths resulting from other causes increased with older age at diagnosis. Patients with one to three positive lymph nodes were more likely to die as a result of breast cancer during the first 10 years of follow-up compared with women without positive lymph nodes. Women with estrogen receptor (ER) -positive tumors had the same risk of dying as a result of breast cancer 5 years after diagnosis compared with women with ER-negative tumors.

CONCLUSION

Lymph node negativity is an important long-term predictor of more favorable prognosis. The nature of the relationship between ER status and risk of dying as a result of breast cancer after 5 years of follow-up requires further investigation. Circulatory system diseases are an important cause of death, especially in women diagnosed with breast cancer at an older age.

Improved web-based calculators for predicting breast carcinoma outcomes

We describe a set of web-based calculators, available at http://www.CancerMath.net , which estimate the risk of breast carcinoma death, the reduction in life expectancy, and the impact of various adjuvant treatment choices. The published SNAP method of the binary biological model of cancer metastasis uses information on tumor size, nodal status, and other prognostic factors to accurately estimate of breast cancer lethality at 15 years after diagnosis. By combining these 15-year lethality estimates with data on the breast cancer hazard function, breast cancer lethality can be estimated at each of the 15 years after diagnosis. A web-based calculator was then created to visualize the estimated lethality with and without a range of adjuvant therapy options at any of the 15 years after diagnosis, and enable conditional survival calculations. NIH population data was used to estimate non-breast-cancer chance of death. The accuracy of the calculators was tested against two large breast carcinoma datasets: 7,907 patients seen at two academic hospitals and 362,491 patients from the SEER national dataset. The calculators were found to be highly accurate and specific, as seen by their capacity for stratifying patients into groups differing by as little as a 2% risk of death, and accurately accounting for nodal status, histology, grade, age, and hormone receptor status. Our breast carcinoma calculators provide accurate and useful estimates of the risk of death, which can aid in analysis of the various adjuvant therapy options available to each patient.