Incidence and Epidemiology

Slide 1 · Breast cancer

Incidence and Epidemiology

Patterns of disease, detection, risk and mortality

Dr Abu Suraih Sakhri

MBBS, MD (Pathology)

www.abusuraihsakhri.com

Slide 2 · What the epidemiology section explains

How age, geography and clinical subtype shape the distribution of breast cancer.

Why observed incidence changes when screening and hormonal exposures change.

How unequal access, tumor biology and screening limitations influence outcomes.

Focus: Invasive carcinoma; Risk-factor context included where necessary.

Slide 3 · Breast cancer became the most commonly diagnosed cancer

2.3 million

Estimated new cases worldwide in 2020

The global estimate represented almost 12% of all new cancer diagnoses.

Breast cancer surpassed lung cancer as the most frequently diagnosed cancer overall in 2020.

Slide 4 · Incidence and mortality are different endpoints

Incidence

Number of newly diagnosed cancers in a defined population and interval.

Affected by both disease occurrence and intensity of detection.

Mortality

Deaths attributable to breast cancer in a defined population and interval.

Influenced by stage at diagnosis, biology and care availability.

A population may have higher recorded incidence without better or worse survival.

Slide 5 · The worldwide burden includes unequal outcomes

Breast cancer is the leading cause of cancer death among females worldwide account.

Survival gains have been achieved mainly in higher-resource settings.

Earlier diagnosis and more effective adjuvant treatment are identified as major contributors.

Global incidence and mortality do not move in lockstep.

Slide 6 · Lifetime risk in the United States

1 in 8

Females living to age 90 years

A cumulative probability across the life course, not an annual probability.

The source explicitly conditions the statement on survival to age 90 years.

Slide 7 · United States: Estimated invasive cancers in 2023

297,790

Estimated new invasive breast cancer diagnoses in females

This is a calendar-year estimate, not a measure of lifetime risk.

The source reports more than 43,000 breast-cancer deaths for 2023.

Slide 8 · Avoid mixing epidemiologic denominators

Lifetime estimate

Cumulative risk over decades in a defined population.

Communicates an individual’s long-term chance of diagnosis.

Annual count

Estimated number of diagnoses or deaths in one year.

Varies with population size and population age structure.

An annual count cannot be directly interpreted as an individual probability.

Slide 9 · High-incidence regions identified

North America.

Northern and Western Europe.

Australia and New Zealand.

These are qualitative regional comparisons; No regional rate values were provided.

Slide 10 · The burden is rising in many low-resource countries

The source describes rising breast cancer incidence in many low-resource settings.

Breast cancer mortality is also increasing in these settings.

The associated social changes and access constraints differ from those in high-resource regions.

Incidence trends require both exposure and detection context.

Slide 11 · Reproductive shifts can increase population risk

Delayed childbearing

Fewer pregnancies

Reduced breastfeeding

These social trends as possible contributors to higher incidence.

They alter the distribution of protective reproductive histories across cohorts.

Slide 12 · Limited access can amplify mortality

Symptoms or lesion present

Presentation at later stage

Limited optimal care

The textbook links late presentation and lack of access to worsening outcomes.

Access acts after disease develops; It is not interchangeable with susceptibility.

Slide 13 · Breast cancer is rare before age 25

The source explicitly characterizes disease in females younger than 25 as rare.

A breast lesion in this age group cannot be presumed malignant from age alone.

Population risk differs from the need to investigate a clinically concerning lesion.

Age modifies prior probability; It is not an exclusion criterion.

Slide 14 · Incidence rises rapidly after age 30

Young adulthood

After age 30

Later decades

A rapid rise after age 30.

Subtype-specific trajectories diverge as patients age.

Slide 15 · Three practical clinical breast cancer groups

Clinical group

ER / HER2 pattern

Epidemiologic significance

Luminal

ER-positive / HER2-negative

Incidence continues rising into later life

HER2-positive

HER2 overexpression; ER may vary

Incidence plateaus by middle age

Triple-negative

ER-, PR-, HER2-

Incidence plateaus by middle age

Clinical subtype labels should not be mistaken for a complete molecular taxonomy.

Slide 16 · Luminal breast cancer: Later-life predominance

ER-positive/HER2-negative tumors are described as the luminal clinical group.

Luminal cancer incidence continues to rise with age.

Its incidence peaks later than that of TNBC or HER2-positive cancer.

Cumulative rise changes the older-age case mix.

Slide 17 · TNBC and HER2-positive cancers plateau earlier

The source describes incidence plateaus by middle age.

This is an age-pattern comparison, not a claim that older adults do not develop these cancers.

The pattern contrasts with the continued later-life increase of luminal tumors.

No numeric age-specific rates were supplied in the available excerpt.

Slide 18 · Aggressive clinical subtypes are prominent in younger females

≈ half

TNBC and HER2-positive together among young-onset cases

These two groups as nearly half of young-female breast cancers.

This is a proportion of cancers in an age stratum, not the absolute population incidence.

Slide 19 · Their proportion falls in older females

<20%

TNBC and HER2-positive together among cancers in older females

Luminal tumors make up an increasing share with advancing age.

The lower share in older patients does not imply absence of TNBC or HER2-positive disease.

Slide 20 · Absolute age-specific rates differ from case proportions

Subtype incidence

Rate of new tumors of a subtype per population at a given age.

The source figure uses rates per 100,000 adult females.

Subtype proportion

Fraction of all breast cancers in an age group belonging to one subtype.

Can change because another subtype becomes more common.

Never reconstruct an age-incidence curve from subtype percentages alone.

Slide 21 · Two factors shaped the apparent rise in the 1980s

Mammographic screening expanded

More cancers detected

Reported incidence increased

The source also names increased postmenopausal hormone therapy exposure.

Recorded incidence reflects both detection practices and disease-associated exposures.

Slide 22 · Screening and hormonal therapy act differently

Mammographic screening

Identifies otherwise asymptomatic cancers.

Can raise recorded case numbers through earlier detection.

Postmenopausal hormone therapy

Population exposure increased during the historical period.

The source identifies its use as a second contributor to incidence change.

Do not attribute an observed incidence increase solely to tumor initiation.

Slide 23 · The incidence of older-age cancers fell after 2000

The source associates the decline with plateauing mammography use.

Mammography coverage was described as about 65–75% of eligible females.

Declining use of postmenopausal hormonal therapy was also implicated.

This is a source-era historical account, not a current coverage estimate.

Slide 24 · Trends differed by age

Older females

An apparent screening-era rise was followed by decline after 2000.

Both screening plateau and hormone therapy changes were cited.

Younger females

Incidence was described as relatively constant over the same period.

The age contrast argues against a single uniform population-wide trend.

Avoid applying an older-age trend to every age group.

Slide 25 · A framework for interpreting surveillance trends

Biologic occurrence

Detection intensity

Observed diagnoses

A change in mammographic screening alters when and whether a cancer is counted.

A change in risk exposure may also change the underlying number of new cancers.

Slide 26 · Screening seeks clinically occult cancers

Mammography is used to find asymptomatic, nonpalpable breast carcinoma.

Mammography as the major screening test.

A screening abnormality initiates further evaluation; It is not by itself a histologic diagnosis.

Detection and diagnosis are separate steps.

Slide 27 · Three principal mammographic signs

Finding

Interpretation

Critical caution

Density

Region of increased attenuation

Not specific for malignancy

Architectural distortion

Disturbed tissue arrangement

Not specific for malignancy

Calcifications

Radiographically visible deposits

Not specific for malignancy

Morphologic and imaging findings require clinicoradiologic assessment.

Slide 28 · A density is a finding, not a diagnosis

A focal or asymmetric density can prompt diagnostic assessment.

Density among the principal signs of carcinoma.

The source explicitly states none of the principal mammographic signs is specific.

Do not assign a carcinoma type from density alone.

Slide 29 · Architectural distortion can be a detection clue

Distortion is listed as a principal mammographic sign of carcinoma.

It indicates abnormal spatial organization rather than a definitive etiology.

Histologic confirmation may be needed when the finding is suspicious.

Image appearance and tissue diagnosis serve distinct roles.

Slide 30 · Calcifications are not inherently malignant

Calcifications are among the major signs recognized on mammography.

The source cautions that no single sign is specific for carcinoma.

The presence of calcification does not determine invasion by itself.

Do not equate “calcification detected” with invasive disease.

Slide 31 · Mammography shifts detection toward smaller tumors

≈1 cm

Mean invasive tumor size at mammographic detection

The source states mammographically detected invasive cancers are smaller on average than palpated cancers.

Tumor size at detection is not interchangeable with biologic indolence.

Slide 32 · Mammography performs differently across age groups

Both sensitivity and specificity increase with age.

Age therefore changes how screening findings should be interpreted.

A positive image finding is not equally predictive of cancer across populations.

Performance must be considered in the screened population.

Slide 33 · Probability that a mammographic lesion is cancer at age 40

10%

Value reported for age 40 years

A lesion seen on a mammogram does not automatically represent cancer.

This estimate is age-contextual and should not be exported as universal test performance.

Slide 34 · The probability rises after age 50

>25%

Reported probability for females older than 50 years

The text contrasts this probability with 10% at age 40.

Age-related prevalence influences the proportion of suspicious findings that are malignant.

Slide 35 · Positive predictive value is not sensitivity

Probability a lesion is cancer

Condition on having a mammographically recognized lesion.

Depends on age and the population undergoing imaging.

Sensitivity of mammography

Condition on truly having cancer.

Describes the test’s ability to detect disease.

Do not interpret the age-specific lesion probabilities as sensitivity.

Slide 36 · Standard two-dimensional imaging is not infallible

≈10%

Invasive carcinomas not visible on standard 2D mammograms

A nonvisualized cancer is an important limitation of standard mammography.

The source names adjunct imaging modalities for selected circumstances.

Slide 37 · Adjunct imaging can address selected blind spots

Standard 2D mammography

Detection limitation persists

Consider adjunct imaging

Digital breast tomosynthesis, ultrasonography and MRI are named adjuncts.

The excerpt does not prescribe a universal imaging algorithm or testing interval.

Slide 38 · Most detected cancers are already invasive

70–80%

Cancers invasive by the time of detection

Earlier diagnosis does not mean that an invasive step has been prevented.

The source notes that some cancers have already metastasized when detected.

Slide 39 · Aggressive disease may arise before screening age

The text highlights lethal cancers that appear in young, prescreening-age females.

These cancers cannot be prevented from presenting by screening initiated later in life.

This is one reason screening has a smaller mortality effect than originally hoped.

Screening eligibility does not determine tumor biology.

Slide 40 · Interval cancers challenge periodic screening

Mammogram without detection

Rapid tumor growth

Clinical presentation between exams

Some aggressive cancers become evident between scheduled mammograms.

A preceding non-diagnostic mammogram does not guarantee absence of future cancer.

Slide 41 · Screening also detects biologically indolent disease

10–30%

Estimated clinically unimportant mammographically detected cancers

Such tumors would not cause harm during the individual’s lifetime if undetected.

The figure is an estimate account, not a patient-specific prediction.

Slide 42 · Breast screening has two contrasting limitations

Under-detection

Prescreening aggressive cancers.

Rapid interval cancers and mammographically occult disease.

Over-detection

Identification of tumors unlikely to cause harm.

Potential downstream investigation and treatment of indolent disease.

Assess benefits and harms without treating all detected cancers as equivalent.

Slide 43 · Incidence varies across ethnic groups in the source

The highest incidence among females of European descent.

Age at presentation and relative subtype composition also differ.

These are population-level observations, not deterministic predictions for a person.

Ancestry and ethnicity categories are context-dependent descriptors.

Slide 44 · Reported average age at diagnosis by group

Average age at diagnosis (years)

63 years

European descent

59 years

African descent

56 years

Hispanic

The group means as 63, 59 and 56 years, respectively.

An average is not a clinical age cutoff or the age of every affected individual.

Slide 45 · Proportion of diagnoses before age 50

Proportion diagnosed before age 50 (%)

20%

European descent

35%

African descent

20%

Hispanic

35% before age 50 in females of African descent versus 20% in each of the other listed groups.

These are within-group proportions, not population incidence rates.

Slide 46 · Luminal cancers contribute to incidence differences

The source attributes much of the excess incidence in females of European descent to luminal tumors.

Luminal disease increases more strongly into later life.

Subtype composition can affect the observed mean age of diagnosis.

Subtype distribution helps explain aggregate epidemiology.

Slide 47 · TNBC and HER2-positive proportions also vary

These groups make up a larger fraction of cases in some non-European-descent populations.

A higher fraction need not mean a higher absolute subtype rate.

Do not infer patient-level biomarker status from demographic group membership.

Individual tumors require ER, PR and HER2 assessment.

Slide 48 · The reported risk of death has declined

1–2%/year

Recent annual decline reported era

The decline is described for those diagnosed with invasive breast carcinoma.

The source links part of mortality improvement to earlier diagnosis and better treatment.

Slide 49 · Overall case-associated death risk in the source era

≈20%

Reported overall risk of death after invasive breast cancer

This is a population summary, not an individualized survival prediction.

Clinical stage, tumor subtype and access to care influence outcomes.

Slide 50 · Mortality gains are not evenly distributed

That declines in mortality lag in females of African descent.

This population is described as having the highest breast cancer mortality rate in the cited context.

The source proposes both disparities in care and differences in tumor subtype as contributors.

Risk disparities are multifactorial.

Slide 51 · Care access contributes to mortality disparities

Inequitable access

Later or suboptimal care

Unequal outcomes

Access is explicitly identified as a partial explanation for the disparity.

This framework should not be confused with a biologically fixed causal effect of ancestry.

Slide 52 · Subtype mix also affects outcomes

More TNBC in a population

More difficult-to-treat biology

Potential outcome gap

The source identifies higher proportions of aggressive tumor types among females of African descent.

Subtype cannot fully explain a disparity without considering care and other determinants.

Slide 53 · Population mortality is shaped by two interacting domains

Tumor domain

ER / PR / HER2 clinical subtype.

Differences in intrinsic growth and natural history.

Health-system domain

Timeliness of diagnosis.

Availability of optimal treatment.

Reducing mortality requires more than a change in screening alone.

Slide 54 · Breast cancer risk is multifactorial

The source highlights sex, age, endogenous estrogen exposure and genetic inheritance.

Family history adds information about inherited susceptibility.

Environmental and lifestyle factors are also implicated but contribute variably.

Risk-factor strength must be separated from modifiability.

Slide 55 · Female sex dominates the recorded case distribution

99%

People affected by breast cancer who are female

The source identifies sex as a major risk determinant.

Breast cancer in males occurs, but represents a small share of diagnoses.

Slide 56 · Age and lifetime estrogen exposure matter

Incidence increases with age, especially after age 30.

The table includes early menarche and late menopause among modest risk factors.

Duration of hormonal exposure is relevant to understanding these associations.

Mechanistic emphasis is hormonal exposure, not a single age threshold.

Slide 57 · Higher relative-risk categories

Relative-risk group

Examples

Interpretation

>4.0

High-penetrance germline variants; Strong family history; Prior breast cancer; High density

Large elevation vs reference

2.1–4.0

Moderate-penetrance variants; High-dose chest radiation at young age; One affected first-degree relative

Intermediate elevation

1.1–2.0

Selected hormonal, reproductive and lifestyle exposures

Modest elevation

Table categories are source-era summaries, not individual prediction tools.

Slide 58 · Markedly elevated-risk profiles

High-penetrance germline mutations and strong family history are listed in the >4.0 group.

Prior personal breast cancer and high breast density also appear in the source’s upper band.

A factor’s population frequency differs from its relative-risk magnitude.

High relative risk does not by itself specify absolute risk.

Slide 59 · Intermediate relative-risk profiles

Moderate-penetrance germline mutations appear in the 2.1–4.0 band.

High-dose irradiation of the chest before age 18 is also in this band.

A family history involving one first-degree relative belongs to this table category.

Risk strata distinguish strength of association, not diagnosis.

Slide 60 · Reproductive and hormone-associated modest risks

Early menarche (younger than 12) and late menopause (older than 55).

First pregnancy after 35, nulliparity or absence of breastfeeding.

Exogenous hormone therapy.

These exposures are listed in the 1.1–2.0 relative-risk band.

Slide 61 · Lifestyle factors in the modest-risk band

Postmenopausal obesity.

Physical inactivity.

High alcohol consumption.

These among relative-risk factors of 1.1–2.0.

Slide 62 · Modifiable exposures contribute to some cancers

≈20%

Breast cancers attributed to modifiable risk factors

The source lists obesity, alcohol consumption and low physical activity.

Attributable fraction is not the percent of individual cases for which causation can be proven.

Slide 63 · A suspected exposure is not an established cause

Supported factors

Obesity, alcohol and inactivity are included as modifiable contributors.

The source reports an aggregate attributable proportion.

Unproved environmental hypothesis

Certain pesticides and plastics may have estrogenic effects.

There is concern but no proof of increased human breast-cancer risk.

Do not translate a hypothesis into a proven etiologic claim.

Slide 64 · Early pregnancy and breastfeeding reduce risk

Pregnancy before age 20 is identified as an important protective factor.

Prolonged breastfeeding is also described as protective.

Shifts toward later childbirth and reduced breastfeeding may change population risk over time.

Risk reduction is a population association, not individual certainty.

Slide 65 · Risk-reducing mastectomy has a large reported effect

≈90%

Reduction in breast-cancer risk after bilateral prophylactic mastectomy

The procedure is generally discussed for females at very high risk.

This is relative risk reduction, not a guarantee of zero residual risk.

Slide 66 · Chemoprevention is subtype-specific

Estrogen receptor antagonists reduce the incidence of ER-positive breast cancers.

The source does not claim equivalent protection against ER-negative tumors.

Pharmacologic prevention is described chiefly for women at very high risk.

Biologic subtype determines the expected preventive effect.

Slide 67 · High-risk intervention and population prevention differ

Individual high-risk strategy

Consider family/genetic context.

Examples: Prophylactic surgery and ER-directed chemoprevention.

Population risk reduction

Addresses common modifiable exposures.

Requires attention to reproductive trends and access disparities.

A dramatic relative effect in a selected individual is not an estimate of population impact.

Slide 68 · Clinical application: Young patient and subtype

A 34-year-old female is diagnosed with invasive breast cancer.

What can age alone tell you about subtype?

TNBC or HER2-positive disease forms a larger proportion of young-onset cases.

Age cannot establish the tumor’s biomarker profile; ER, PR and HER2 require tissue-based evaluation.

Hypothetical teaching scenario — not a patient record.

Slide 69 · Clinical application: Screening-negative cancer

A patient presents with a palpable lesion after a recent unremarkable mammogram.

Does the prior mammogram exclude invasive carcinoma?

No. Some invasive cancers are occult on standard 2D mammography.

Aggressive cancers may also become clinically evident between scheduled examinations.

Hypothetical teaching scenario — not a patient record.

Slide 70 · Clinical application: Comparing populations

Two groups have different average ages at breast-cancer diagnosis and unequal mortality.

Can the difference be assigned only to inherited biology?

No. The source identifies subtype mix and unequal access to optimal care.

Population-level descriptors do not establish the cause of risk for a particular patient.

Hypothetical teaching scenario — not a patient record.

Slide 71 · Integrated teaching conclusions

Age, reproductive exposure, subtype biology and geography shape incidence.

Screening changes ascertainment but misses some lethal disease and detects some indolent disease.

Observed mortality reflects both tumor biology and health-system access.

Risk-factor magnitude, modifiability and preventive intervention must be interpreted separately.

Epidemiology is the intersection of susceptibility, detection and clinical outcome.

Slide 72 · Primary source and scope of estimates

Robbins, Cotran & Kumar. Pathologic Basis of Disease. 11th edition (2026). Chapter 23, The Breast, pp. 954–956.

The numerical values dated 2020 and 2023 reflect the historical estimates reproduced in the textbook.

All diagrams in this presentation are original editable teaching schematics; no clinical images or reconstructed textbook curves are used.

No current-year incidence, mortality, screening-policy or guideline update was inferred from the textbook excerpt.