Evidence map›Paper›PMID 42761746›Full record

ReviewFrontiers in oncology2026

Lobular breast cancer and prognosis: what should we tell patients?

Tivya Kulasegaran, Kate Beecher, Pamela Kinnon, Sunil R Lakhani, Peter T Simpson, Amy E McCart Reed

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tivya KulasegaranFraser Institute, Faculty of Health, Medicine and Behavioural Science, The University of Queensland, Brisbane, QLD, Australia.
Kate BeecherFraser Institute, Faculty of Health, Medicine and Behavioural Science, The University of Queensland, Brisbane, QLD, Australia.
Pamela KinnonLobular Breast Cancer Alliance Australia and New Zealand, Brisbane, QLD, Australia.
Sunil R LakhaniFraser Institute, Faculty of Health, Medicine and Behavioural Science, The University of Queensland, Brisbane, QLD, Australia.
Peter T SimpsonFraser Institute, Faculty of Health, Medicine and Behavioural Science, The University of Queensland, Brisbane, QLD, Australia.
Amy E McCart ReedFraser Institute, Faculty of Health, Medicine and Behavioural Science, The University of Queensland, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Invasive lobular carcinoma (ILC) is the second most common subtype of breast cancer. It accounts for up to 15% of all breast cancer diagnoses, and there is an increasing population of people living with a diagnosis of 'lobular'. ILC has historically been understudied and is underrepresented in clinical trials. Presenting with pathology features typical of a good prognosis (oestrogen receptor-positive, HER2-negative, grade 2), a diagnosis of ILC can be framed as one with a 'good outcome'. But whether the data support this over time remains to be seen. Methods: We present a comprehensive narrative review to ascertain whether ILC has a better outcome than invasive carcinoma of no special type (IC-NST). Results: A number of the research studies analysed showed that ILC can have a better outcome at 5 years and still have a poorer outcome over time, whilst others showed that IC-NST has a poor outcome early, which is maintained over time. Conclusions: We discuss limitations and confounders and, critically, give voice to the people living with a lobular breast cancer diagnosis.

Indexed as

breast cancerILCinvasive lobular carcinomalobularoutcomeprognosissurvival

Identifiers

PMID42761746
PMCPMC13587032

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.