Dataset Information

Grading Ductal Carcinoma In Situ (DCIS) of the Breast - What's Wrong with It?

ABSTRACT: Ductal carcinoma in situ of the breast is a non-obligate precursor of invasive breast cancer, and at its lower risk end might not need treatment, a hypothesis tested in several currently running randomized clinical trials. This review describes the heterogeneity of grading ductal carcinoma in situ (DCIS). First it considers differences between low and high grade DCIS, and then it looks at several grading schemes and highlights how different these are, not only in the features considered for defining a given grade but also in their wording of a given variable seen in the grade in question. Rather than being fully comprehensive, the review aims to illustrate the inconsistencies. Reproducibility studies on grading mostly suggestive of moderate agreement on DCIS differentiation are also illustrated. The need for a well structured, more uniform and widely accepted language for grading DCIS is urged to avoid misunderstanding based misclassifications and improper treatment selection.

SUBMITTER: Cserni G

PROVIDER: S-EPMC7242244 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Grading Ductal Carcinoma In Situ (DCIS) of the Breast - What's Wrong with It?

Cserni Gábor G Sejben Anita A

Pathology oncology research : POR 20191127 2

Ductal carcinoma in situ of the breast is a non-obligate precursor of invasive breast cancer, and at its lower risk end might not need treatment, a hypothesis tested in several currently running randomized clinical trials. This review describes the heterogeneity of grading ductal carcinoma in situ (DCIS). First it considers differences between low and high grade DCIS, and then it looks at several grading schemes and highlights how different these are, not only in the features considered for defi ...[more]

PMID: 31776839

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Project description:Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer where cells restricted to the ducts exhibit an atypical phenotype. Some DCIS lesions are believed to rapidly transit to invasive ductal carcinomas (IDCs), while others remain unchanged. Existing classification systems for DCIS fail to identify those lesions that transit to IDC. We studied gene expression patterns of 31 pure DCIS, 36 pure invasive cancers and 42 cases of mixed diagnosis (invasive cancer with an in situ component) using Agilent Whole Human Genome Oligo Microarrays 44k. Six normal breast tissue samples were also included as controls. qRT-PCR was used for validation. All DCIS and invasive samples could be classified into the intrinsic molecular subtypes defined for invasive breast cancer. Hierarchical clustering establishes that samples group by intrinsic subtype, and not by diagnosis. We observed heterogeneity in the transcriptomes among DCIS of high histological grade and identified a distinct subgroup containing seven of the 31 DCIS samples with gene expression characteristics more similar to advanced tumours. A set of genes independent of grade, ER-status and HER2-status was identified by logistic regression that univariately classified a sample as belonging to this distinct DCIS subgroup. qRT-PCR of single markers clearly separated this DCIS subgroup from the other DCIS, and contains samples from several histopathological and intrinsic molecular subtypes. The genes that differentiate between these two types of DCIS suggest several processes related to the re-organisation of the microenvironment. This raises interesting possibilities for identification of DCIS lesions both with and without invasive characteristics, which potentially could be used in clinical assessment of a woman's risk of progression, and lead to improved management that would avoid the current over- and under-treatment of patients. Breast cancer samples, 31 pure DCIS patients, 36 IDC patients, 42 mixed and 6 normal.

Project description:Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

Dataset Information

Grading Ductal Carcinoma In Situ (DCIS) of the Breast - What's Wrong with It?

Publications

Grading Ductal Carcinoma In Situ (DCIS) of the Breast - What's Wrong with It?

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