The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin

Doval, Dinesh Chandra; Mehta, Anurag; Somashekhar, S. P.; Gunda, Aparna; Singh, Gurpreet; Bal, Amanjit; Khare, Siddhant; Serkad, Chandra Prakash V; Adinarayan, Manjula; Krishnamoorthy, Naveen; Vijay, D. G.; Anantakrishnan, Radha; Bhattacharyya, G.S.; Bakre, Manjiri M.

doi:10.1016/j.breast.2021.05.007

Cited by 7 publications

(17 citation statements)

References 24 publications

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“…With respect to clinical outcomes CAB had a higher distant metastasis-free survival (DMFS) (95.6%) compared to PREDICT (90.7%) demonstrating that CAB low-risk predictions correlate with better survival outcomes than PREDICT (data not shown). Published data shows that the CAB ‘low risk’ patients with T1N0 disease have an excellent DMFS of 98% [ 17 ]. Though NPI, PREDICT, and CAB risk prediction models all have tumor size, node status, tumor grade as common parameters, the utmost important feature present additionally in CAB which is missing in NPI/PREDICT is the analysis of tumor biology.…”

Section: Discussionmentioning

confidence: 99%

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Gunda¹,

Eshwaraiah²,

Gangappa³

et al. 2022

Breast Cancer Res Treat

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Aims Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2− breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts. Methods Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41–5.4), and poor (PPG > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient. Results Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278–0.346)]/PREDICT [κ = 0.398 (0.35–0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB. Conclusion Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.

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Section: Discussionmentioning

confidence: 99%

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Gunda¹,

Eshwaraiah²,

Gangappa³

et al. 2022

Breast Cancer Res Treat

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“…Further, only 6 models (4.4%) constructed for cancer outcomes in LLMIC populations have been externally validated (phase III implementation) all of which were within the last five years. These included models for breast cancer diagnosis ( 85 , 86 , 152 ), lung cancer diagnosis ( 51 , 52 ), head and neck cancer diagnosis ( 124 ), breast cancer metastasis ( 47 , 56 , 128 ), liver cancer risk prediction ( 78 ), and treatment response in colorectal cancer ( 148 ). Of these models, only two ( 47 , 56 , 85 , 152 ) sufficiently fulfilled the TRIPOD criteria for external validation based on the sample size.…”

Section: Resultsmentioning

confidence: 99%

“…These included models for breast cancer diagnosis ( 85 , 86 , 152 ), lung cancer diagnosis ( 51 , 52 ), head and neck cancer diagnosis ( 124 ), breast cancer metastasis ( 47 , 56 , 128 ), liver cancer risk prediction ( 78 ), and treatment response in colorectal cancer ( 148 ). Of these models, only two ( 47 , 56 , 85 , 152 ) sufficiently fulfilled the TRIPOD criteria for external validation based on the sample size. Also, none of the deep learning or deep hybrid learning models found have been assessed using external validation.…”

Section: Resultsmentioning

confidence: 99%

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Adeoye

Akinshipo²,

Koohi‐Moghadam³

et al. 2022

Front. Oncol.

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BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

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“…CAB predicts the risk of distant recurrence within five years from diagnosis in early-stage, HR+/HER2− breast cancer patients. CAB has been validated on breast cancer patient cohorts from India, the USA, and Europe showcasing identical performance on 5-year recurrence risk predictions across multiple races/ethnicities [8,10,11]. It was interesting to see 83-85% concordance in the low-risk group between CAB and Oncotype DX/ MammaPrint, the highest ever shown between any two prognostic tests [11,12].…”

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confidence: 97%

“…CanAssist Breast (CAB), a prognostic test was developed based on three clinical parameters (tumor size, grade, and axillary lymph nodes) and the expression of five biomarkers (CD44, N-Cadherin, pan Cadherin, ABCC4 and ABCC11) quantitated using immunohistochemistry and scoring by pathologists [6]. CAB uses a Support Vector Machine Learning (SVML)-based algorithm to predict risk score and category (high or low) [7][8][9]. CAB predicts the risk of distant recurrence within five years from diagnosis in early-stage, HR+/HER2− breast cancer patients.…”

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confidence: 99%

Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial

Zhang

Gunda²,

Kranenbarg

et al. 2023

Breast Cancer Res

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Background Hormone receptor (HR)-positive, HER2/neu-negative breast cancers have a sustained risk of recurrence up to 20 years from diagnosis. TEAM (Tamoxifen, Exemestane Adjuvant Multinational) is a large, multi-country, phase III trial that randomized 9776 women for the use of hormonal therapy. Of these 2754 were Dutch patients. The current study aims for the first time to correlate the ten-year clinical outcomes with predictions by CanAssist Breast (CAB)—a prognostic test developed in South East Asia, on a Dutch sub-cohort that participated in the TEAM. The total Dutch TEAM cohort and the current Dutch sub-cohort were almost similar with respect to patient age and tumor anatomical features. Methods Of the 2754 patients from the Netherlands, which are part of the original TEAM trial, 592 patients’ samples were available with Leiden University Medical Center (LUMC). The risk stratification of CAB was correlated with outcomes of patients using logistic regression approaches entailing Kaplan–Meier survival curves, univariate and multivariate cox-regression hazards model. We used hazard ratios (HRs), the cumulative incidence of distant metastasis/death due to breast cancer (DM), and distant recurrence-free interval (DRFi) for assessment. Results Out of 433 patients finally included, the majority, 68.4% had lymph node-positive disease, while only a minority received chemotherapy (20.8%) in addition to endocrine therapy. CAB stratified 67.5% of the total cohort as low-risk [DM = 11.5% (95% CI, 7.6–15.2)] and 32.5% as high-risk [DM = 30.2% (95% CI, 21.9–37.6)] with an HR of 2.90 (95% CI, 1.75–4.80; P < 0.001) at ten years. CAB risk score was an independent prognostic factor in the consideration of clinical parameters in multivariate analysis. At ten years, CAB high-risk had the worst DRFi of 69.8%, CAB low-risk in the exemestane monotherapy arm had the best DRFi of 92.7% [vs CAB high-risk, HR, 0.21 (95% CI, 0.11–0.43), P < 0.001], and CAB low-risk in the sequential arm had a DRFi of 84.2% [vs CAB high-risk, HR, 0.48 (95% CI, 0.28–0.82), P = 0.009]. Conclusions Cost-effective CAB is a statistically robust prognostic and predictive tool for ten-year DM for postmenopausal women with HR+/HER2−, early breast cancer. CAB low-risk patients who received exemestane monotherapy had an excellent ten-year DRFi.

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The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin

Cited by 7 publications

References 24 publications

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial

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