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> Disorders > Neoplastic Process > Ductal Carcinoma

Ductal Carcinoma

Ductal Carcinoma: A type of breast cancer that originates in the milk ducts.
It is the most common form of breast cancer, accounting for approximately 80% of all breast cancer cases.
Ductal carcinoma can be either in situ (DCIS), where the cancer cells are contained within the milk duct, or invasive (IDC), where the cancer cells have spread beyond the duct into the surrounding breast tissue.
Early detection and prompt treatment are crucial for managing ductal carcinoma and improving patient outcomes.
PubCompare.ai's innovative AI-driven tools can help researchers optimize their ductal carcinoma studies by locating the most relevant protocols from literature, preprints, and patents, as well as identify the best products and protocols using advanced comparisons - enhancing reproducibility and accuracy in this important area of breast cancer research.

Most cited protocols related to «Ductal Carcinoma»

1
Establishment and Characterization of Pancreatic Cancer Cell Lines
Cited 13 times
The human pancreatic adenocarcinoma cell lines AsPC-1, CaPan-1, MiaPaca-2, and PANC-1 were purchased from the American Type Culture Collection (Manassas, VA); MDA Panc-28, and MDA Panc-48 were gifts from Dr. Paul J. Chiao (M.D. Anderson Cancer Center); and COLO357 human pancreatic adenocarcinoma metastasis and its fast-growing (FG) and liver metastatic variant L3.3 and L3.7 in nude mice, and the murine ductal adenocarcinoma cell line Panc02 and its highly metastatic variant Panc02-H7 were described previously (23 (link)–25 (link)). All of these cell lines were maintained in plastic flasks as adherent monolayers in Eagle's minimal essential medium supplemented with 10% fetal bovine serum, sodium pyruvate, nonessential amino acids, L-glutamine, and vitamin solution (Flow Laboratories). The immortalized normal human pancreatic ductal epithelial cell line HPDE (provided by Dr. Tsao, Ontario Cancer Institute) was maintained in keratinocyte serum-free medium supplemented with epidermal growth factor and bovine pituitary extract (Invitrogen, Carlsbad, CA).
Huang C., Qiu Z., Wang L., Peng Z., Jia Z., Logsdon C., Le X., Wei D., Huang S, & Xie K. (2011). A novel FoxM1-Caveolin signaling pathway promotes pancreatic cancer invasion and metastasis. Cancer Research, 72(3), 655-665.
Publication 2011
Adenocarcinoma Amino Acids Bos taurus Cell Lines Cells Ductal Carcinoma Epidermal growth factor Fetal Bovine Serum Gifts Glutamine Homo sapiens Keratinocyte LINE-1 Elements Liver Malignant Neoplasms Mice, Nude Mus Neoplasm Metastasis Pancreas Pyruvate Serum Sodium Vitamins
2
Differential Gene Expression Analysis
Cited 10 times
Scanned images of microarray chips were analysed by the GCOS (GeneChip Operating Software) from Affymetrix with the default settings except that the target signal was set to 100. Differentially expressed genes between cell types were identified using the GCOS change algorithm and Rank Products (RP) [40 (link)] following RMA (Robust Multiarray Analysis) [41 (link)]. GCOS pairwise analysis was performed to compare gene expression levels among the normal lobular, normal ductal and tumor cells within individual patients and between lobular and ductal carcinoma cells of different patients. For each comparison between two cell types, the number of increase and decrease calls of each probe set was calculated using MS Excel and probe sets with the highest number of consistent changes among all patients were identified. Probe level quantile normalization [40 (link)] and robust multiarray analysis [41 (link)] on the raw. CEL files were performed using the Affymetrix package of the Bioconductor [42 ]. The cutoff value of percentage of false-positives for RP analysis was set to 10%. Gene lists were uploaded to DAVID (Database for Annotation, Visualization and Integrated Discovery) [43 ], and functional annotation was performed. Further informations on genes were obtained from public databases, such as Gene Cards [44 ] and NETAFFX Analysis Center [45 ]. Hierarchical clustering was performed using dChip software [46 ]. The data discussed in this publication have been deposited in NCBI Gene Expression Omnibus (GEO) [47 ], and are accessible through GEO Series accession number GSE5764.
Turashvili G., Bouchal J., Baumforth K., Wei W., Dziechciarkova M., Ehrmann J., Klein J., Fridman E., Skarda J., Srovnal J., Hajduch M., Murray P, & Kolar Z. (2007). Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis. BMC Cancer, 7, 55.
Publication 2007
Cells DNA Chips Ductal Carcinoma Gene Chips Gene Expression Genes Microarray Analysis Neoplasms Patients Somatostatin-Secreting Cells
3
Prostate Cancer Histologic Patterns Analysis
Cited 9 times
Patients’ entire prostatectomy slide sets were rereviewed. The slides containing cancer (average ± SD per case, 8.0 ± 4.3) were digitally scanned as virtual slides using a ScanScope XT (Aperio Technologies, Vista, CA). By using ImageScope software (Aperio), we manually annotated all foci of 9 histologic patterns in a nonoverlapping manner, using a different color for each pattern, denoted as follows: (1) S, single, separate small acini, like 3B pattern3 (link) without blue mucin Image 1A; (2) B, single, separate small acini with blue mucin Image 1B; (3) U, undulated, stellate, or branching medium acini, like 3A pattern3 (link) lined by a single cell layer Image 1C; (4) F, fused, ragged small acini, including those with mucin Image 1D; (5) P, (micro)papillary consisting of medium to large spaces with stromal cores or strands of cells with 1 or more cell layers bridging across the acinus, with intervening slit-like spaces Image 1E; (6) SC, small cribriform, mediumsized acinar spaces with rounded contour, no solid foci, and 12 or fewer lumen spaces (inclusive of the glomeruloid pattern18 (link)) Image 1F; (7) LC, large cribriform, with expansive cribriform to focally solid large acini with more than 12 lumen spaces Image 1G; (8) I, individual infiltrating or sheet-like cells lacking lumen formation Image 1H; and (9) M, mucinous (colloid) carcinoma in which nonfused acini (excluding single cells) floated in mucin pools Image 1I. Note that although the lumen contours of the U and P patterns were somewhat similar, strict criteria (as stated) were adhered to for P, (micro)papillary. Based on the assertion that small, rounded cribriform cancer might be graded as Gleason grade 3,10 (link),13 (link),15 (link) specimens with any cribriform pattern were further subdivided by annotating the SC and LC areas. Image 2 shows an example of an annotated slide.
The rare cancer subtypes of ductal19 (link),20 (link) (excluding some papillary patterns that were encompassed in a broader definition of ductal21 (link)), and adenoid cystic/basal cell carcinoma22 (link) were denoted, if present, according to established criteria. To verify all annotations considered to be LC or SC or to have ductal cancer, consensus conferences were held by 4 of us (K.A.I., G.R.K., F.G.L., and M.S.L.), at which annotated slide images were projected on a screen. For each specimen, taking together all pertaining slides, the area sum of each cancer pattern was determined by summation of all digitally annotated foci belonging to that pattern. In this manner, each distinct pattern could be considered individually from a statistical viewpoint. The amount of each pattern per specimen was also expressed as percentage of the total cancer area. However, area sums rather than pattern percentages were used as the main method of analysis to approximate the cancer contribution of each pattern.
To determine the relative tendency of each pattern toward extraprostatic extension or seminal vesicle invasion, the cancer patterns responsible for these findings were tabulated. As a separate analysis, to explore the effect of having a sizable amount of large acinar (LA, or P plus cribriform [C] patterns) cancer, a cut point of P + C patterns forming more than one third of the total cancer area per patient was chosen. The one-third cut point was chosen for its ease of perception at diagnosis and because it produced a category that included 17 cases, allowing meaningful statistical comparison with the other 136.
Iczkowski K.A., Torkko K.C., Kotnis G.R., Wilson R.S., Huang W., Wheeler T.M., Abeyta A.M., La Rosa F.G., Cook S., Werahera P.N, & Lucia M.S. (2011). Digital Quantification of Five High-Grade Prostate Cancer Patterns, Including the Cribriform Pattern, and Their Association With Adverse Outcome. American journal of clinical pathology, 136(1), 98-107.
Publication 2011
Adenoids ARID1A protein, human Carcinoma, Cribriform Cells Conferences Cyst Diagnosis Ductal Carcinoma Malignant Neoplasms Mucinous Adenocarcinoma Mucins Patients Prostatectomy Seminal Vesicles
4
Profiling Transcriptional Changes in Triple-Negative Breast Cancer
Cited 5 times
Accession number: The microarray data set from TN patients, before and after chemotherapy (excluding patients with complete response to treatment), is freely accessible in the Gene Expression Omnibus under the accession number: GSE43816.
TN ductal carcinomas were analysed on Human Gene 1.1ST arrays (Affymetrix), before and after chemotherapy. The data were analysed using Partek Genomic Suite version 6.6. Normalization was performed using RMA background correction with pre‐background adjustment for GC content. Exons were summarized to genes using average method. Only probes with a log2 intensity > 5.2 in at least 5% of all samples were kept for further analysis. A differential analysis using a paired‐samples t‐test was applied in order to detect genes differentially expressed before and after neo‐adjuvant chemotherapy within the same patient. DAVID functional annotation tool was then used to identify significant enriched Gene Ontology pathways significantly upregulated after chemotherapy. Fold enrichment is defined as the ratio between input genes and background information. P‐values are calculated using a hypergeometric‐based method (Huang da et al, 2009).
Gruosso T., Mieulet V., Cardon M., Bourachot B., Kieffer Y., Devun F., Dubois T., Dutreix M., Vincent‐Salomon A., Miller K.M, & Mechta‐Grigoriou F. (2016). Chronic oxidative stress promotes H2AX protein degradation and enhances chemosensitivity in breast cancer patients. EMBO Molecular Medicine, 8(5), 527-549.
Publication 2016
Chemotherapy, Adjuvant Ductal Carcinoma Exons Gene Expression Genes Genome Microarray Analysis Patients Pharmacotherapy
5
Multisite Evaluation of Breast Cancer Assay
Cited 5 times
This comparative three-site study used replicate breast tumor tissue specimens from the same FFPE block for testing with the MammaTyper (Table 2). A 16-member panel of breast cancer samples (15 ductal carcinomas and 1 lobular carcinoma) obtained from commercial vendors except for one clinical sample and comprising all different tumor subtypes were analyzed. All tissue specimens were sectioned at BioNTech Diagnostics GmbH. The first and last section were analysed with MammaTyper at site 1 and showed nearly identical marker expression. After rearranging, sections were shipped to the other testing sites (Stratifyer Molecular Pathology GmbH, Cologne and Institute of Pathology, University of Erlangen). Each tissue sample was processed during three, 4-day cycles, starting with RNA extraction and aliquoting of eluates (day 1) followed by three RT-qPCR runs on consecutive days (day 2–4). The procedure was performed by one operator per site using a single instrument (Versant kPCR Cycler, Siemens), a single lot of RNXtract and a single lot of the MammaTyper Assay. At BioNTech Diagnostics six additional cycles were performed by the same operator, two with different MammaTyper lots, one with an alternative RNXtract lot and three on a LightCycler 480 II (Roche) qPCR device to account for related effects on precision.
Intra-run precision was estimated based on the variance of triplicate measurements. All calculations for the precision studies were carried out based on CLSI guideline EP 05-A3 [25 ] using a random effects model II ANOVA via PROC mixed in SAS Version 9.2.
Laible M., Schlombs K., Kaiser K., Veltrup E., Herlein S., Lakis S., Stöhr R., Eidt S., Hartmann A., Wirtz R.M, & Sahin U. (2016). Technical validation of an RT-qPCR in vitro diagnostic test system for the determination of breast cancer molecular subtypes by quantification of ERBB2, ESR1, PGR and MKI67 mRNA levels from formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer, 16, 398.
Publication 2016
Biological Assay Breast Carcinoma Breast Neoplasm Carcinoma, Lobular Diagnosis DNA Replication Ductal Carcinoma Medical Devices Neoplasms neuro-oncological ventral antigen 2, human Tissues

Most recents protocols related to «Ductal Carcinoma»

1
Pancreatic Ductal Adenocarcinoma RNA-seq Analysis
The bulk gene expression and survival data (n = 182) of TCGA pancreatic cancer were downloaded from the UCSC data portal ( https://xenabrowser.net ). The raw count data obtained from the RNA-sequencing (RNA-seq) was normalized concerning library size using the Deseq2 package, thus making fair gene comparisons between samples. After excluding cases not histologically diagnosed as primary ductal cancer and deficient in survival data, we reserved 147 PDAC patients for further analysis.
Yang K., Yang T., Yu J., Li F, & Zhao X. (2023). Integrated transcriptional analysis reveals macrophage heterogeneity and macrophage-tumor cell interactions in the progression of pancreatic ductal adenocarcinoma. BMC Cancer, 23, 199.
Publication 2023
Anophthalmia with pulmonary hypoplasia Dietary Fiber DNA Library Ductal Carcinoma Gene Expression Genes Pancreatic Cancer Patients
2
Evaluating SegPath Segmentation Model
To test the generalization performance of the SegPath dataset, we applied the models trained on the SegPath dataset to two external validation datasets: (1) GlaS dataset8 (link) for epithelium and (2) NuCLS dataset7 (link) for lymphocyte semantic segmentation. For GlaS, both training and test data, which consisted of 165 images from 74 benign and 91 malignant colon tissue images in total, were processed with the model with no training on the GlaS dataset. Because our semantic segmentation model for epithelia is not directly applicable for instant segmentation, individual gland information is not used in the evaluation. For the NuCLS dataset, a corrected single raster dataset, which consists of 452 patches with more than five lymphocytes from The Cancer Genome Atlas cases, was tested. For the GlaS dataset, Dice coefficient of the segmentation mask was used for the evaluation. In contrast, because NuCLS contains both segmentation mask for the segmentation and boundary box for detection, object-level Dice coefficient was used for the evaluation, where the object in the prediction is defined as the consecutive positive region, and any overlap between the contour and the segmentation mask or boundary box in the ground truth is considered as true positive. The only difference between image pre-processing and applying the models to the SegPath was scaling based on the mpp ratio (2.8 for GlaS and 0.9049 for NuCLS). The model ensemble approach for epithelium and lymphocytes using two or three different models with optimal validation Dice coefficients during training (Table S5) was used for the segmentation.
WSIs from two institutes were used for the validation of multi-cell-type segmentations of H&E-stained images. Specimens were obtained from (1) three patients with gastric adenocarcinoma who underwent surgery and were diagnosed at the University of Tokyo Hospital between 1955 and 2018 and (2) four patients with salivary gland tumors (salivary duct carcinoma, Warthin’s tumor, and cystadenoma) who underwent surgery and were diagnosed at Tokyo Medical and Dental University Hospital between 1990 and 2020. Resected specimens of gastric adenocarcinoma were prepared from the FFPE blocks and sliced to a thickness of 6 μm. All histopathological specimens were anonymized. This study was approved by the Institutional Review Board of each university. Throughout these experiments, the multi-cell-type segmentation strategy described below was used.
Komura D., Onoyama T., Shinbo K., Odaka H., Hayakawa M., Ochi M., Herdiantoputri R.R., Endo H., Katoh H., Ikeda T., Ushiku T, & Ishikawa S. (2023). Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists. Patterns, 4(2), 100688.
Publication 2023
Adenocarcinoma Adenolymphoma Carcinoma Cells Colon Cystadenoma Dental Health Services Ductal Carcinoma Epithelium Ethics Committees, Research Generalization, Psychological Genome Lymphocyte Malignant Neoplasms Operative Surgical Procedures Patients Salivary Ducts Salivary Gland Neoplasms Stomach Tissues
3
Breast Cancer Patients' Cardiac Health
From the preliminary selected population (n = 102), 8 patients were excluded because the hematocrit examinations were performed outside the indicated time interval (more than 4 weeks before or after the CT examination), 2 patients because the CT scans were incomplete, 4 patients because the CT images were compromised by motion artefacts, 2 patients who had already received radiotherapy for left breast cancer, 1 patient who had already received another cardiotoxic chemotherapy, 2 patients who had a previous heart disease (FE < 40%) and 1 patient because the chemotherapy dose had to be reduced to 75% during treatment due to febrile neutropenia. After applying the exclusion criteria, the population considered for the final analyses accounted for 82 patients (Figure 2 and Table 1).
At the time of diagnosis, the population median age was 53 years (IQR 47.5–58.5), with 62 patients affected by ductal carcinoma, 4 patients by infiltrating lobular carcinoma, 14 patients by a poorly differentiated carcinoma and 2 patients by an undifferentiated carcinoma. About 44 patients reported a stage II tumor (following TNM 8th edition), 16 presented with a stage IV and 14 stage III and 8 stage Ic (24). Of the patients, 54 were treated with doxorubicin (DOX)-based treatment and 28 with a epirubicin–trastuzumab (EPI–TRAS) based treatment. All patients were healthy at T0, with hematocrit and LVEF mean values of 38.8% ± 4% and 64% ± 5%, respectively (Table 1). At the ECV follow-up after 5 years, all 82 patients were considered, as they adequately fit the criteria.
Chiocchi M., Cerocchi M., Di Tosto F., Rosenfeld R., Pasqualetto M., Vanni G., De Stasio V., Pugliese L., Di Donna C., Idone G., Muscoli S., Portarena I., Roselli M., Garaci F, & Floris R. (2023). Quantification of Extracellular Volume in CT in Neoadjuvant Chemotherapy in Breast Cancer: New Frontiers in Assessing the Cardiotoxicity of Anthracyclines and Trastuzumab. Journal of Personalized Medicine, 13(2), 199.
Publication 2023
Breast Breast Carcinoma Carcinoma Carcinoma, Lobular Cardiotoxicity Diagnosis Doxorubicin Ductal Carcinoma Epirubicin Febrile Neutropenia Heart Diseases Neoplasms Patients Pharmacotherapy Physical Examination Radiotherapy Trastuzumab Undifferentiated Carcinoma Volumes, Packed Erythrocyte X-Ray Computed Tomography
4
Neoadjuvant Chemotherapy for Locally Advanced Breast Cancer

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Publication 2023
Breast Carcinoma Carcinoma, Lobular Ductal Carcinoma Lung Cancer Neoadjuvant Chemotherapy Neoplasm Metastasis Neoplasms Patients Scan, CT PET Woman
5
Breast Cancer Prognostic Factors
The following variables at diagnosis were selected as the potential prognostic factors: age(less than 60 years old or older than 60 years old), laterality (right or left side), pathological type (infiltrative ductal carcinoma or other types), histological grading (well-differentiated, moderately differentiated, poorly differentiated, undifferentiated or anaplastic), tumor size, lymph node metastasis status, distant metastatic status, type of surgery, chemotherapy (yes or none) and radiotherapy (yes or none). The values of tumor size, lymph node metastasis status, distant metastasis status, and surgery type were transformed into grouped categorical variables according to routine practice.
Overall survival (OS) was used as the primary endpoint for this study. OS was defined as the time between the date of diagnosis and the date of death caused by BC. For the validation cohort, the deadline for follow-up was September 14, 2021.
Geng A., Xiao J., Dong B, & Yuan S. (2023). Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer. Frontiers in Oncology, 13, 1071076.
Publication 2023
Anaplasia Diagnosis Ductal Carcinoma Lymph Node Metastasis Neoplasm Metastasis Neoplasms Operative Surgical Procedures Pharmacotherapy Prognostic Factors Radiotherapy

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