Breast cancer: not one disease, and the argument about finding it early

Topic: breast cancer: molecular subtypes, mammographic screening and overdiagnosis, subtype-directed therapy, and Gompertzian growth · Since 1990 · Grounded citations only · Published 2026-08-23

Start here: what breast cancer is

Breast cancer is a malignancy arising from breast tissue — usually from the cells lining the ducts or the milk-producing lobules. It is the most commonly diagnosed cancer in women worldwide and a leading cause of cancer death; the GLOBOCAN 2022 estimates put it among the largest contributors to the global cancer burden across 185 countries [1], and projections of the current and future burden run out to 2040 [2].

The single most important thing to understand is that it is not one disease. Two tumours that look similar under a microscope can behave completely differently, respond to completely different drugs, and carry completely different prognoses, depending on which molecules they express. Since the early 2000s the field has organised itself around intrinsic molecular subtypes — luminal A, luminal B, HER2-enriched, basal-like — identified from gene-expression portraits and later reduced to a practical classifier [3] [4] [5] [6]. In everyday practice those subtypes are approximated by three receptors measured on the biopsy:

The WHO classification of breast tumours codifies the pathology behind this [8]. The practical consequence is that "breast cancer" names a category of decisions, not a single diagnosis, and every section below splits along those lines.

Why it is in the news, perennially. Two reasons, and they are different in kind. The first is a genuine, unresolved public argument about mammographic screening — how much benefit it delivers, at what cost in overdiagnosis, and at which ages and intervals. That argument has been going for decades and is quantified rather than settled [9] [10] [11]. The second is that the treatment side has moved very fast: antibody–drug conjugates have just redrawn the boundary of who counts as "HER2-positive" at all [12].

Its sibling in this series. The colorectal cancer review covers the other great screening-programme cancer, and the logic there — lead time, sojourn time, overdiagnosis, interval cancers — is the same logic that runs through this review's centrepiece. Obesity appears here as a risk and prognostic factor [13] [14] [15].

Three pillars follow — measurements, medicines, and progress — with a tumour-growth model between the first two that explains exactly what screening can and cannot catch.

Pillar 1: measurements and diagnosis

Mammographic screening, and the actual size of the argument

The case for screening is that finding a cancer before it declares itself allows treatment to work better. The case against is not that screening finds nothing — it is that screening finds too much, including cancers that would never have harmed the woman in her lifetime.

The most careful public accounting is the UK independent panel convened under Michael Marmot, and its numbers are worth stating precisely because they are so often mangled. From meta-analysis of 11 randomised trials with 13 years of follow-up, screening invitation was associated with a 20% reduction in breast cancer mortality (95% CI 11–27%). Applied to UK absolute risks, that means about one breast cancer death prevented for every 235 women invited, or 180 women actually screened. On the harm side, using the three RCTs that did not systematically screen the control group at the end, the frequency of overdiagnosis was "of the order of 11% from a population perspective, and about 19% from the perspective of a woman invited to screening" [9].

Both of those are real and both are large. The Cochrane review reaches a more sceptical reading of the same trials [10]; the American Cancer Society guideline reaches a more favourable one for average-risk women [11]. An analysis of three decades of US screening found that the incidence of early-stage disease roughly doubled while the incidence of late-stage disease fell only slightly — the signature of substantial overdiagnosis [16]. Estimates vary widely depending on study features and methods, and that variation has itself been studied [17].

The honest summary is that screening does reduce breast cancer mortality, that it also diagnoses cancers that would never have mattered, and that the ratio between the two depends on age, interval and the assumptions used. The model below is about why those two things travel together.

Diagnosis and staging

A suspicious finding on imaging leads to biopsy, which establishes whether cancer is present, its histological type and grade, and — critically — the receptor status above. Disease extent is described by TNM staging: the size and local extent of the tumour, whether regional lymph nodes are involved, and whether there are distant metastases. Nodal status is assessed surgically, and the extent of axillary surgery has itself been progressively de-escalated as evidence accumulated [18].

The measurements that choose the treatment

Three tiers of measurement now sit between diagnosis and a treatment plan.

Receptor status, as above, sorts patients into the three therapeutic worlds. Grade and Ki-67, a proliferation marker, further separate the slow-growing luminal A pattern from the more proliferative luminal B pattern, which carries a worse prognosis at the same receptor status [19].

Genomic recurrence scores answer a narrower and more agonising question: for a woman with hormone-receptor-positive, node-negative disease, will adding chemotherapy to endocrine therapy actually help? The 21-gene recurrence score was validated prospectively — in the low-risk group assigned to endocrine therapy alone, outcomes at five years were excellent without chemotherapy [20], and real-world cohorts have reported consistent results [21]. Professional bodies have issued guidance on which biomarkers should and should not drive adjuvant decisions [22] [23] [24]. The same logic has now been extended to omitting radiotherapy in women with low clinical and genomic risk [25].

Hereditary risk. Pathogenic variants in BRCA1 and BRCA2 confer substantially elevated lifetime risk and change both screening and treatment decisions; beyond those two genes, association analysis has identified 65 further breast cancer risk loci, so common polygenic risk is real but individually small [26]. BRCA1 mutation and young age together predict faster tumour growth on MRI screening [27] — a fact that matters directly for the model below.

Centerpiece: a simple simulatable model of tumour growth and the screening window

Screening arguments usually get conducted in words. They are much clearer as arithmetic, and the arithmetic needs only one idea: tumours do not grow exponentially — they decelerate.

Fitting exponential, Gompertz and seven generalised logistic equations to serial mammographic measurements in 448 patients, all the decelerating equations fit better than the exponential [28]. Norton's Gompertzian model of human breast cancer growth is the parsimonious version of that idea, and it makes explicit clinical projections about "the estimated duration of silent growth prior to diagnosis" [29]. Write it in cell number:

V(t) = V_max · exp( −ln(V_max/V₀) · exp(−b·t) )

with V₀ = a single cell and V_max = 2⁴⁰ cells, both the assumptions used in the fitting study [28]. That leaves one rate constant b, and it does not have to be guessed: it can be solved so the tumour takes exactly the time a population study measured.

0 2 4 6 8 10 12 14 years since the first malignant cell 0 10 20 30 40 tumour diameter, mm 5 mm — screen sensitivity 26% 10 mm — screen sensitivity 91% 1.7 yr 1.3 yr the same eightfold increase in volume takes 1.3 yr from 5→10 mm but 1.7 yr from 10→20 mm — growth decelerates; it is not exponential single cell → 5 mm takes 6 years, longer than the entire visible phase Gompertzian growth: decelerating, and mostly invisible 0.1 0.5 1 2 5 10 20 years for a tumour to grow from 10 mm to 20 mm (log scale) density of tumours 5% faster than 1.2 months 5% slower than 6.3 years fitted to those two tails alone, this distribution's mean is 1.75 yr — the published mean is 1.7 cross the whole window faster than the interval: ≤ 1 yr 57% ≤ 2 yr 77% ≤ 3 yr 85% Screening interval meets tumour biology
Computed Gompertzian growth and the screening window it implies. LEFT PANEL: tumour diameter against years since the first malignant cell, under V(t) = Vmax*exp(-ln(Vmax/V0)*exp(-b t)) with V0 = one cell and Vmax = 2^40 cells, the assumptions used when exponential, Gompertz and seven generalised logistic forms were fitted to serial mammographic measurements in 448 patients - a study in which every decelerating equation beat the exponential (Spratt et al., Cancer 1993 [W2078348833]); the Gompertzian model itself is Norton's (Cancer Res 1988 [W2144418840]). The rate constant b is SOLVED, not chosen, so the tumour takes exactly the 1.7 years that 395,188 women in the Norwegian Breast Cancer Screening Program gave as the mean time to grow from 10 mm to 20 mm (Weedon-Fekjaer et al., Breast Cancer Res 2008 [W2109278571]). Two consequences are asserted in the script: the same eightfold increase in volume takes 1.3 years from 5 to 10 mm but 1.7 years from 10 to 20 mm, so growth demonstrably decelerates; and reaching even 5 mm takes about 6 years from the first cell, longer than the entire subsequent visible phase. Dotted lines mark 5 mm and 10 mm with their measured screen sensitivities of 26% and 91% from the same study. EXTERNAL CHECK: the implied local doubling time in the screening window is 207 days, within a factor of 1.6 of the 130-day median doubling time reported for an independent population (Michaelson et al., 2003 [W2081906980]). RIGHT PANEL: individual tumours vary enormously. A lognormal distribution of 10-to-20 mm transit times is fitted to two published tails ONLY - 5% take under 1.2 months, another 5% take over 6.3 years [W2109278571]. Fitted to those tails alone, its mean comes out at 1.75 years against the published mean of 1.7 that was never given to the fit, and the script asserts that agreement. Dashed lines at 1, 2 and 3-year screening intervals show that 57%, 77% and 85% of tumours respectively cross the entire 10-to-20 mm window faster than the interval. ILLUSTRATIVE and flagged: the conversion from cell counts to diameters uses a conventional 10^6 cells per mm3, not a measured value; a single curve stands in for a "typical" tumour when the sources stress that kinetic heterogeneity is intrinsic; and the lognormal FORM is an assumption fitted to two quantiles, not a published finding. Screening also detects DCIS and does not select purely on size, and nothing here models overdiagnosis, which the review treats separately.

What the model explains. Four things, and each is one side of the screening argument.

First, why most of a cancer's life is invisible. On this curve a tumour takes about six years to reach 5 mm — where mammographic sensitivity is only 26% — and it reaches 10 mm, where sensitivity is 91%, at about seven years [30]. The silent phase is longer than the entire detectable phase. Screening is not catching disease "early" in any absolute sense; it is catching it a year or two earlier than symptoms would.

Second, why growth deceleration matters for what screening buys. The same eightfold increase in volume takes 1.3 years from 5→10 mm but 1.7 years from 10→20 mm. Under exponential growth those would be equal. Because the tumour slows as it enlarges, the detectable window is wider than exponential intuition suggests — which helps screening — but the same deceleration means that a tumour detected small may have been present for years, which is part of why earlier detection does not translate one-for-one into lives saved.

Third, why the screening interval is the whole design question. The transit-time distribution is enormously wide: 5% of tumours cross from 10 mm to 20 mm in under 1.2 months, and another 5% take more than 6.3 years [30]. Fitted to those two tails, 57% of tumours cross that window in under a year, 77% in under two, 85% in under three. Tumours in the fast tail can appear and grow past the window entirely between two screening rounds — they present symptomatically as interval cancers, which have systematically worse characteristics and outcomes than screen-detected ones [32]. Shortening the interval catches more of them, at the cost of more screens and more overdiagnosis.

Fourth, why screening preferentially finds the tumours that matter least. This is the uncomfortable corollary. A slow tumour spends years in the detectable window and will be caught by almost any screening programme; a fast one may never be caught at all. Screening therefore over-samples indolent disease — which is the mechanism of the 11–19% overdiagnosis the Marmot panel quantified [9], and the reason early-stage incidence doubled without a matching fall in late-stage disease [16]. The two headline findings of the screening debate are not in tension; they are the same curve seen from two ends.

What the model deliberately does not do. It has one curve where the sources insist heterogeneity is intrinsic [29], and growth rate demonstrably varies with age and genotype — younger women and BRCA1 carriers have faster-growing tumours [30] [27] [33]. It selects on size alone, when real screening also detects DCIS and is affected by breast density. It contains no treatment, so it cannot say how much of the mortality benefit is earlier detection versus better therapy — a distinction the Marmot panel flagged as central, noting that treatment advances have had "a demonstrably larger influence on mortality trends than does screening" [9]. And it says nothing about the woman's experience of a diagnosis she did not need.

Pillar 2: medicines

Treatment is local plus systemic. Local control is surgery — breast-conserving or mastectomy — plus radiotherapy, whose effect is well quantified: the EBCTCG meta-analysis of trials of radiotherapy after mastectomy and axillary surgery measured its effect on 10-year recurrence and 20-year breast cancer mortality [18]. Systemic therapy is where the subtypes diverge completely.

Hormone-receptor-positive disease: starve it

If a tumour depends on oestrogen, remove the signal. Two mechanisms do this.

Tamoxifen blocks the estrogen receptor directly. The EBCTCG overview of the randomised trials established its effect on recurrence and mortality in early breast cancer [34], and it is effective enough that it reduced breast cancer incidence in high-risk women in the NSABP P-1 prevention trial [35].

Aromatase inhibitors — anastrozole, letrozole, exemestane — instead block aromatase, the enzyme that synthesises oestrogen from androgens, which in postmenopausal women is the main remaining source. ATAC compared anastrozole against tamoxifen and against the combination as adjuvant therapy [36]; BIG 1-98 compared letrozole with tamoxifen [37]; MA.17 tested letrozole after five years of tamoxifen and found further benefit, establishing extended adjuvant therapy [38] [39]. ASCO issued a technology assessment on their adjuvant use [40].

Resistance eventually develops, and its mechanism is partly known: acquired mutations in ESR1 itself can be detected in plasma and predict outcome in ER-positive advanced disease [41]. Adding an mTOR inhibitor, everolimus, to endocrine therapy extended progression-free survival in that setting [42].

HER2-positive disease: target it

This is the field's clearest demonstration that a molecular target can be turned into a drug. Trastuzumab is a monoclonal antibody against HER2. In metastatic disease, adding it to chemotherapy lengthened time to progression (7.4 vs 4.6 months), raised response rate (50% vs 32%), extended median survival (25.1 vs 20.3 months) and reduced the risk of death by 20% — with cardiac dysfunction as the important toxicity, in 27% of those also receiving an anthracycline [7].

Moved into the adjuvant setting the effect was larger. The joint analysis of NSABP B-31 and NCCTG N9831 reported a hazard ratio of 0.48 for recurrence, second primary or death, a 12-percentage-point absolute improvement in three-year disease-free survival, and a 33% reduction in the risk of death [43]; HERA confirmed benefit for trastuzumab given after adjuvant chemotherapy [44]. Later agents extended the approach: lapatinib, a small-molecule kinase inhibitor, for trastuzumab-refractory disease [45] and combined with letrozole in HR-positive/HER2-positive disease [46]; and trastuzumab emtansine (T-DM1), the first antibody–drug conjugate in this space, in previously treated advanced disease [47]. The whole trajectory has been reviewed as a model for targeted oncology [48] [49].

HR-positive/HER2-negative advanced disease: block the cell cycle

The newest pillar in this group inhibits CDK4 and CDK6, the kinases that drive cells past the G1 restriction point, and it works specifically in combination with endocrine therapy. PALOMA-1 first showed longer progression-free survival for palbociclib plus letrozole [50]; PALOMA-2 confirmed it in phase 3, with median progression-free survival of 24.8 versus 14.5 months (hazard ratio 0.58, 95% CI 0.46–0.72), at the cost of substantial neutropenia — grade 3–4 in 66.4% versus 1.4% [51]. PALOMA-3 established benefit with fulvestrant after progression on endocrine therapy [52] [53], later with an overall survival readout [54], and MONARCH 3 showed the same for abemaciclib as initial therapy [55].

Triple-negative disease: chemotherapy, and now immunotherapy

Lacking all three targets, this subtype depended on cytotoxic chemotherapy alone for decades. Checkpoint inhibition changed that. Early signals came from KEYNOTE-012 [56], and KEYNOTE-522 established the approach in early disease: adding pembrolizumab to neoadjuvant chemotherapy raised the pathological complete response rate from 51.2% to 64.8% [57], with event-free survival benefit confirmed on longer follow-up [58]. In advanced disease, KEYNOTE-355 showed benefit for pembrolizumab plus chemotherapy [59] [60]. Not every trial in the class succeeded — IMpassion131 was negative for atezolizumab with paclitaxel [61] — which is a useful reminder that the chemotherapy backbone and the biomarker definition both matter.

Management of advanced disease across all subtypes has been codified in international consensus guidelines [62], and survivorship care in its own guideline [63] — including the cardiovascular consequences of the treatments above [64].

Pillar 3: progress

Antibody–drug conjugates, and the erasure of a category

The most consequential recent change is conceptual as much as pharmacological. Trastuzumab deruxtecan (T-DXd) couples the anti-HER2 antibody to a potent topoisomerase inhibitor with a high drug-to-antibody ratio and a membrane-permeable payload, so it kills neighbouring cells as well as the one it binds. It showed striking activity in previously treated HER2-positive disease [65] [66] [67], and then beat T-DM1 head-to-head in DESTINY-Breast03 [68] [69].

The category-breaking result was DESTINY-Breast04. Among breast cancers without HER2 amplification or overexpression, a large proportion express low levels of HER2 — defined as an immunohistochemistry score of 1+, or 2+ with negative in-situ hybridisation — and "currently available HER2-directed therapies have been ineffective" in them. T-DXd was tested against physician's-choice chemotherapy in 557 patients with HER2-low metastatic disease, 88.7% of them hormone-receptor-positive, and improved progression-free survival [12]. Activity has since been shown across HER2-expressing solid tumours generally [70].

The significance is that "HER2-positive" was never a property of tumours; it was a threshold on an assay, chosen because that was the level at which the old drugs worked. A better drug moved the threshold, and a large group of patients previously classified as HER2-negative became treatable.

Risk-stratified screening

If the model's message is that a single interval applied to everyone is a blunt instrument, the response is to vary it. Risk-stratified screening — using age, density, family history, and polygenic risk to set each woman's interval and modality — has been assessed for cost-effectiveness and harm–benefit ratio [71] [72], and the WISDOM trial was designed to test personalised against annual screening, with simulation work assessing its power in advance [73]. Deep learning applied to screening mammograms improves detection [74], which changes the sensitivity term in the same arithmetic.

De-escalation

Progress is not only additive. Because the measurements in Pillar 1 identify low-risk disease reliably, it is now possible to remove treatment: omitting chemotherapy on the basis of a genomic recurrence score [20] [22], and omitting radiotherapy after breast-conserving surgery in women with low clinical and genomic risk [25]. For a disease whose treatments carry lasting cardiac and other costs [64], subtracting therapy safely is as much a gain as adding it.

Risk factors that are actually modifiable

Body weight is the clearest. A systematic review and meta-analysis of 82 follow-up studies examined body mass index and survival in women with breast cancer [13], with a separate meta-analysis reaching the same direction [14], and national cancer statistics have been reported specifically featuring cancers associated with excess weight and insufficient activity [15]. Risk factors and prevention have been reviewed broadly [75] [76], as has public awareness of them [77].

Dig deeper in lmmol

Breast cancer sits alongside the other screening-programme cancer and the metabolic risk factor it shares with much of this collection:

  • Colorectal cancer — the oncology and screening sibling. Lead time, sojourn time, interval cancers and overdiagnosis are the same machinery there, applied to a different organ and a different test.
  • Obesity — a risk and prognostic factor here, quantified across 82 follow-up studies [13].
  • The health reviews index collects the rest of the series.

Then move down into lmmol's graph, to the receptors and enzymes that decide the treatment:

Key papers

  1. W4393935425: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries (cited 24,886×)
  2. W4294189863: Current and future burden of breast cancer: Global statistics for 2020 and 2040 (cited 3,353×)
  3. W2166311434: Deconstructing the molecular portraits of breast cancer (cited 1,341×)
  4. W2132619562: Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes (cited 4,942×)
  5. W2141414987: Clinical implications of the intrinsic molecular subtypes of breast cancer (cited 1,329×)
  6. W2158794266: Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways (cited 631×)
  7. W2318605753: Use of Chemotherapy plus a Monoclonal Antibody against HER2 for Metastatic Breast Cancer That Overexpresses HER2 (cited 11,584×)
  8. W3006138035: The 2019 World Health Organization classification of tumours of the breast (cited 1,103×)
  9. W2113622720: The benefits and harms of breast cancer screening: an independent review (cited 1,084×)
  10. W1549906179: Screening for breast cancer with mammography (cited 1,876×)
  11. W2155329254: Breast Cancer Screening for Women at Average Risk (cited 1,667×)
  12. W4281640394: Trastuzumab Deruxtecan in Previously Treated HER2-Low Advanced Breast Cancer (cited 2,644×)
  13. W2149466392: Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies (cited 1,181×)
  14. W1974196451: Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis (cited 983×)
  15. W1700373378: Annual Report to the Nation on the status of cancer, 1975‐2008, featuring cancers associated with excess weight and lack of sufficient physical activity (cited 460×)
  16. W2169666080: Effect of Three Decades of Screening Mammography on Breast-Cancer Incidence (cited 1,382×)
  17. W2130224358: Influence of Study Features and Methods on Overdiagnosis Estimates in Breast and Prostate Cancer Screening (cited 132×)
  18. W2129825983: Effect of radiotherapy after mastectomy and axillary surgery on 10-year recurrence and 20-year breast cancer mortality: meta-analysis of individual patient data for 8135 women in 22 randomised trials (cited 2,283×)
  19. W2127057139: Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer (cited 2,239×)
  20. W2190954851: Prospective Validation of a 21-Gene Expression Assay in Breast Cancer (cited 1,450×)
  21. W2753267414: Clinical outcomes in patients with node-negative breast cancer treated based on the recurrence score results: evidence from a large prospectively designed registry (cited 70×)
  22. W2267844613: Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline (cited 824×)
  23. W2593104572: Clinical use of biomarkers in breast cancer: Updated guidelines from the European Group on Tumor Markers (EGTM) (cited 542×)
  24. W2777329839: Biomarkers in breast cancer: A consensus statement by the Spanish Society of Medical Oncology and the Spanish Society of Pathology (cited 78×)
  25. W4389452822: Omission of Radiotherapy After Breast-Conserving Surgery for Women With Breast Cancer With Low Clinical and Genomic Risk: 5-Year Outcomes of IDEA (cited 100×)
  26. W2765253743: Association analysis identifies 65 new breast cancer risk loci (cited 1,598×)
  27. W1991216698: BRCA1 Mutation and Young Age Predict Fast Breast Cancer Growth in the Dutch, United Kingdom, and Canadian Magnetic Resonance Imaging Screening Trials (cited 116×)
  28. W2078348833: Decelerating growth and human breast cancer (cited 199×)
  29. W2144418840: A Gompertzian model of human breast cancer growth. (cited 695×)
  30. W2109278571: Breast cancer tumor growth estimated through mammography screening data (cited 205×)
  31. W2081906980: Estimates of Breast Cancer Growth Rate and Sojourn Time from Screening Database Information (cited 35×)
  32. W3088525786: Incidence, Characteristics, and Outcomes of Interval Breast Cancers Compared With Screening-Detected Breast Cancers (cited 101×)
  33. W2072609916: Age-dependent growth rate of primary breast cancer (cited 239×)
  34. W1562190372: Tamoxifen for early breast cancer: an overview of the randomised trials (cited 4,112×)
  35. W2141647078: Tamoxifen for Prevention of Breast Cancer: Report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study (cited 5,525×)
  36. W1453600927: Anastrozole alone or in combination with tamoxifen versus tamoxifen alone for adjuvant treatment of postmenopausal women with early breast cancer: first results of the ATAC randomised trial (cited 1,964×)
  37. W1531964529: A Comparison of Letrozole and Tamoxifen in Postmenopausal Women with Early Breast Cancer (cited 1,585×)
  38. W2101881391: A Randomized Trial of Letrozole in Postmenopausal Women after Five Years of Tamoxifen Therapy for Early-Stage Breast Cancer (cited 1,830×)
  39. W2142739119: Randomized Trial of Letrozole Following Tamoxifen as Extended Adjuvant Therapy in Receptor-Positive Breast Cancer: Updated Findings from NCIC CTG MA.17 (cited 1,122×)
  40. W2151447010: American Society of Clinical Oncology Technology Assessment on the Use of Aromatase Inhibitors As Adjuvant Therapy for Postmenopausal Women With Hormone Receptor–Positive Breast Cancer: Status Report 2004 (cited 1,034×)
  41. W2415372857: Plasma ESR1 Mutations and the Treatment of Estrogen Receptor–Positive Advanced Breast Cancer (cited 726×)
  42. W2167188058: Everolimus in Postmenopausal Hormone-Receptor–Positive Advanced Breast Cancer (cited 2,850×)
  43. W2146384083: Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer (cited 5,369×)
  44. W2149908785: Trastuzumab after Adjuvant Chemotherapy in HER2-Positive Breast Cancer (cited 5,119×)
  45. W2110444464: Lapatinib plus Capecitabine for HER2-Positive Advanced Breast Cancer (cited 3,478×)
  46. W2097698108: Lapatinib Combined With Letrozole Versus Letrozole and Placebo As First-Line Therapy for Postmenopausal Hormone Receptor–Positive Metastatic Breast Cancer (cited 1,029×)
  47. W2159967578: Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer (cited 3,832×)
  48. W4308766741: Targeting HER2-positive breast cancer: advances and future directions (cited 1,129×)
  49. W2976352116: HER2-targeted therapies — a role beyond breast cancer (cited 1,092×)
  50. W2144790402: The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study (cited 1,925×)
  51. W2552099557: Palbociclib and Letrozole in Advanced Breast Cancer (cited 3,015×)
  52. W2290950904: Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, double-blind, phase 3 randomised controlled trial (cited 1,854×)
  53. W1464936406: Palbociclib in Hormone-Receptor–Positive Advanced Breast Cancer (cited 1,599×)
  54. W2896846857: Overall Survival with Palbociclib and Fulvestrant in Advanced Breast Cancer (cited 1,311×)
  55. W2763875663: MONARCH 3: Abemaciclib As Initial Therapy for Advanced Breast Cancer (cited 1,747×)
  56. W2345415641: Pembrolizumab in Patients With Advanced Triple-Negative Breast Cancer: Phase Ib KEYNOTE-012 Study (cited 1,488×)
  57. W3006930853: Pembrolizumab for Early Triple-Negative Breast Cancer (cited 3,228×)
  58. W4210957314: Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer (cited 1,192×)
  59. W3109853980: Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (KEYNOTE-355): a randomised, placebo-controlled, double-blind, phase 3 clinical trial (cited 1,846×)
  60. W4285992810: Pembrolizumab plus Chemotherapy in Advanced Triple-Negative Breast Cancer (cited 1,159×)
  61. W3175173749: Primary results from IMpassion131, a double-blind, placebo-controlled, randomised phase III trial of first-line paclitaxel with or without atezolizumab for unresectable locally advanced/metastatic triple-negative breast cancer (cited 745×)
  62. W2560635244: 3rd ESO–ESMO International Consensus Guidelines for Advanced Breast Cancer (ABC 3) (cited 2,070×)
  63. W2193066862: American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline (cited 747×)
  64. W2791340070: Cardiovascular Disease and Breast Cancer: Where These Entities Intersect: A Scientific Statement From the American Heart Association (cited 849×)
  65. W2996630100: Trastuzumab Deruxtecan in Previously Treated HER2-Positive Breast Cancer (cited 2,010×)
  66. W2760931533: Safety, pharmacokinetics, and antitumour activity of trastuzumab deruxtecan (DS-8201), a HER2-targeting antibody–drug conjugate, in patients with advanced breast and gastric or gastro-oesophageal tumours: a phase 1 dose-escalation study (cited 507×)
  67. W3013632168: Targeting HER2 with Trastuzumab Deruxtecan: A Dose-Expansion, Phase I Study in Multiple Advanced Solid Tumors (cited 358×)
  68. W4220964921: Trastuzumab Deruxtecan versus Trastuzumab Emtansine for Breast Cancer (cited 1,170×)
  69. W4311116389: Trastuzumab deruxtecan versus trastuzumab emtansine in patients with HER2-positive metastatic breast cancer: updated results from DESTINY-Breast03, a randomised, open-label, phase 3 trial (cited 668×)
  70. W4387865238: Efficacy and Safety of Trastuzumab Deruxtecan in Patients With HER2-Expressing Solid Tumors: Primary Results From the DESTINY-PanTumor02 Phase II Trial (cited 1,023×)
  71. W3031116885: Risk stratification in breast cancer screening: Cost‐effectiveness and harm‐benefit ratios for low‐risk and high‐risk women (cited 32×)
  72. W2890163109: Assessing the Cost-Effectiveness of Updated Breast Cancer Screening Guidelines for Average-Risk Women (cited 32×)
  73. W2908811486: The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches (cited 45×)
  74. W2907066882: Deep Learning to Improve Breast Cancer Detection on Screening Mammography (cited 849×)
  75. W2770397194: Risk Factors and Preventions of Breast Cancer (cited 2,037×)
  76. W3193347331: Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review (cited 1,923×)
  77. W2762285338: Awareness and current knowledge of breast cancer (cited 1,418×)