Synchronous double primary small cell lung cancer and invasive ductal breast carcinoma: a case report.

BACKGROUND: Although lung and breast cancers are common malignancies, the occurrence of primary synchronous neoplasms involving these organs has been rarely reported in literature. CASE PRESENTATION: A 75-year-old female patient presented at a local hospital with a ten-day history of dizziness and slurred speech. A CT contrast-enhanced scan revealed a 4.2 cm mass in the lower lobe of the right lung and a 3.8 cm space-occupying lesion in the right breast. Subsequent breast ultrasound identified a hypoechoic lesion measuring5.41 × 4.75 × 3.06 cm in the right breast, and an ultrasound-guided biopsy confirmed the presence of infiltrating ductal carcinoma of the right breast. The immunohistochemistry analysis of the breast mass revealed positive staining for ER, PR, HER-2, AR and Ki67 in the tumor cells, while negative staining was observed for P63, Calponin, CK5/6 and CK14. MR imaging of the head detected abnormal signals in the right frontal lobe (3.6 cm×2.9 cm in size), left cerebellar hemisphere, and punctate enhancement in the left temporal lobe, indicating potential metastasis. Pathological examination of a lung biopsy specimen confirmed the presence of small cell lung cancer (SCLC). Furthermore, immunohistochemistry analysis of the lung lesions demonstrated positive staining for TTF-1, CK-Pan, Syn, CgA, CD56, P53 (90%) and Ki67 (70%), and negative staining for NapsinA and P40 in the tumor cells. The patient's diagnosis of SCLC with stage cT2bN0M1c IVB and brain metastases (BM), as well as invasive ductal breast carcinoma (IDC), was confirmed based on the aforementioned results. Whereupon we proposed a treatment plan consisting of whole-brain radiation (40 Gy/20fractions), focal radiotherapy (60 Gy/20fractions), and adjuvant concurrent chemotherapy with oral etoposide (50 mg on days 1 to 20). CONCLUSIONS: To the best of our knowledge, the present case is the first of its kind to describe the synchronous double cancer, consisting of primary SCLC and IDC.

Case Report of MPO+ ANCA Vasculitis with Pauci-immune GN Associated with Invasive Ductal Carcinoma of the Breast.

BACKGROUND: Malignancy-associated vasculitis usually presents in the form of polyarteritis nodosa or leukocytoclastic vasculitis. However, ANCA vasculitis associated with malignancy is rare. Here, we present a case of MPO+ ANCA vasculitis with pauci-immune GN associated with invasive ductal carcinoma of the breast. CASE PRESENTATION: A 66-year-old female with a history of rheumatoid arthritis, Hashimoto's thyroiditis, and psoriasis presented with multiple joint pain, body aches, petechial rash, paresthesia and numbness, and deranged renal function a month after diagnosis of localized left breast invasive ductal carcinoma. Renal biopsy showed crescentic pauci-immune glomerulonephritis, and serology was positive for Perinuclear Antineutrophil Cytoplasmic Antibody (P-ANCA) and myeloperoxidase (MPO). The disease course was complicated by diverticulitis with peritonitis and intraperitoneal abscess collection, which required laparoscopic peritoneal lavage and additional interventional radiology-guided drainage of the abscess. We treated the patient successfully with steroids, rituximab, and mastectomy for left breast malignant lesions, resulting in the resolution of symptoms, normalization of inflammatory markers, and ANCA seroconversion. CONCLUSION: Treating ANCA-associated Vasculitis (AAV) in surgical emergencies like bowel perforation can be challenging. Individualized treatment strategy tailored to patients' acute needs is crucial. In this case, we considered malignancy-associated vasculitis and pursued treatment that fit the patient's clinical situation in a multidisciplinary approach.

Application Value of Ultrasound Elastography Combined With Contrast-Enhanced Ultrasound (CEUS) Quantitative Analysis in Differentiation of Nodular Fibrocystic Changes of the Breast From Invasive Ductal Carcinoma.

This study aimed to compare the value of ultrasound elastography combined with contrast-enhanced ultrasound (CEUS) quantitative analysis in the differentiation of nodular fibrocystic breast change (FBC) from breast invasive ductal carcinoma (BIDC). We selected 50 patients each with nodular FBC and BIDC, who were admitted to the Affiliated Hospital of Zunyi Medical University from January 2018 to December 2021. Their ultrasonic elastic images and CEUS videos were collected, their ultrasound elastography scores and the ratio of strain rate (SR) of the lesions were determined, and the exported DICOM format videos of CEUS were quantitatively analyzed using VueBox software to obtain quantitative perfusion parameters. The differences between the ultrasound elastography score and SR while comparing nodular FBC and BIDC cases were statistically significant (p < .05). The sensitivity, specificity, and accuracy of ultrasound elastography scores in the differential diagnoses of nodular FBC and BIDC were 74%, 88%, and 81%, respectively. Additionally, the sensitivity, specificity, and accuracy of SR in the differential diagnosis of nodular FBC and BIDC were 94%, 78%, and 86%, respectively. Statistically significant differences were observed in the CEUS quantitative perfusion parameters PE, AUC (WiAUC, WoAUC, WiWoAUC), and WiPI in both nodular FBC and BIDC according to the VueBox software (p < .05). The sensitivity, specificity, and accuracy of CEUS quantitative analysis in the differential diagnoses of nodular FBC and BIDC were 66%, 82%, and 74%, respectively. Using the pathological findings as the gold standard, ROC curves were established, and the area under the curve (AUC) of the CEUS quantitative analysis, elasticity score, SR, and ultrasound elastography combined with CEUS quantitative analysis were 0.731, 0.838, and 0.892, as well as 0.945, respectively. Ultrasound elasticity scoring, SR and CEUS quantitative analysis have certain application value for differentiating nodular FBC cases from BIDC; however, ultrasound elasticity imaging combined with CEUS quantitative analysis can help in improving the differential diagnostic efficacy of nodular FBC cases from BIDC.

End-to-end deep learning radiomics: development and validation of a novel attention-based aggregate convolutional neural network to distinguish breast diffuse large B-cell lymphoma from breast invasive ductal carcinoma.

Background: Apart from invasive pathological examination, there is no effective method to differentiate breast diffuse large B-cell lymphoma (DLBCL) from breast invasive ductal carcinoma (IDC). In this study, we aimed to develop and validate an effective deep learning radiomics model to discriminate between DLBCL and IDC. Methods: A total of 324 breast nodules from 236 patients with baseline 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) were retrospectively analyzed. After grouping breast DLBCL and breast IDC patients, external and internal datasets were divided according to the data collected by different centers. Preprocessing was then used to process the original PET/CT images and an attention-based aggregate convolutional neural network (AACNN) model was designed. The AACNN model was trained using patches of CT or PET tumor images and optimized with an improved loss function. The final ensemble predictive model was built using distance weight voting. Finally, the model performance was evaluated and statistically verified. Results: A total of 249 breast nodules from Fudan University Shanghai Cancer Center (FUSCC) and 75 breast nodules from Shanghai Proton and Heavy Ion Center (SPHIC) were selected as internal and external datasets, respectively. On the internal testing, our method yielded an area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and harmonic mean of precision and sensitivity (F1) of 0.886, 83.0%, 80.9%, 85.0%, 84.8%, 81.2%, and 0.828, respectively. Meanwhile on the external testing, the results were 0.788, 71.6%, 61.4%, 84.7%, 84.0%, 62.6%, and 0.709, respectively. Conclusions: Our study outlines a deep learning radiomics method which can automatically, noninvasively, and accurately differentiate breast DLBCL from breast IDC, which will be more in line with the needs and strategies of precision medicine, individualized diagnosis, and treatment.

Subtrochanteric fracture in previously treated breast cancer patient handled by proximal femoral nail: A case report.

INTRODUCTION: Breast cancer (BC) is the most common and high mortality rate cancer in females. The main complication of BC is metastases, where bone metastases (BM) are present in 90 % of women with distant metastases and commonly recurrence after BC therapy. However, treatment options are numerous, and improving patients' quality of life (QoL) is a priority. PRESENTATION OF CASE: A 58-year-old female patient presented to the emergency department with pain and movement restriction in the right lower extremity after minor trauma. Clinical history included a surgically resected BC eight years ago, besides chemotherapy and radiotherapy. After clinical and radiographic examination, we encountered a subtrochanteric femoral fracture although the patient is in the end stage, the multidisciplinary team discussed the surgery option with the patient and eventually internally fixed the fracture. DISCUSSION: Subtrochanteric femur fractures represent a challenging orthopedic issue, ranging from 10 % to 34 % of all hip fractures. Hence, after a detailed discussion, the proximal femoral nail (PFN) was the procedure of choice acording to the patient's preferences and tumor prognosis. Proximal femoral metastasis treatment aims to improve the quality of life (QoL), alleviate bone pain, and rehabilitate skeletal function. CONCLUSION: In this case report, we highlight the surgical decision consequences for a patient with end-stage cancer, as it may put their life at risk or improve their QoL, likewise the patient in this report.

Analysis of primary synchronous breast invasive ductal carcinoma and lung adenocarcinoma with next-generation sequencing: A case report.

Multiple primary cancers (MPCs) have an increasing incidence rate due to the detection of early stages of cancer and the development of effective therapeutic strategies. MPCs are less common compared with metachronous cancers. Therefore, distinguishing synchronous primary tumors from metastasis and developing an individualized treatment strategy can be challenging. In the present study, the case of a 70-year-old female who was referred to The First Hospital of Jilin University (Changchun, China) with an enlarged left cervical lymph node and no other clinical manifestations is reported. Radiography revealed distinct lesions in the left breast, left cervical lymph node and bilateral lungs. Subsequently, a biopsy was performed in all three lesions and then each specimen was subjected to immunohistochemistry, fluorescence in situ hybridization, amplification refractory mutation system-PCR and next-generation sequencing (NGS). Disease-related enrichment of lymph node mutant genes and Gene Ontology Biological Process enrichment of breast, as well as lung, mutant genes were performed using the Database for Annotation, Visualization and Integrated Discovery. Based on the molecular assessment, the patient was finally diagnosed with breast invasive ductal carcinoma, primary lung adenocarcinoma and cervical lymph node metastatic lung adenocarcinoma. Since primary synchronous breast and lung cancer (SBLC) is rare, a molecular assessment, particularly using NGS, could provide important information for both the diagnosis and treatment of SBLC.

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound.

Purpose: The aim of this study was to develop a radiomics nomogram based on grayscale ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with pathologically confirmed T1 (pT1) breast invasive ductal carcinoma (IDC). Methods: One hundred and ninety-two patients with pT1 IDC between September 2020 and August 2022 were analyzed retrospectively. Study population was randomly divided in a 7: 3 ratio into a training dataset of 134 patients (37 patients with LVI-positive) and a validation dataset of 58 patients (19 patients with LVI-positive). Clinical information and conventional US (CUS) features (called clinic_CUS features) were recorded and evaluated to predict LVI. In the training dataset, independent predictors of clinic_CUS features were obtained by univariate and multivariate logistic regression analyses and incorporated into a clinic_CUS prediction model. In addition, radiomics features were extracted from the grayscale US images, and the radiomics score (Radscore) was constructed after radiomics feature selection. Subsequent multivariate logistic regression analysis was also performed for Radscore and the independent predictors of clinic_CUS features, and a radiomics nomogram was developed. The performance of the nomogram model was evaluated via its discrimination, calibration, and clinical usefulness. Results: The US reported axillary lymph node metastasis (LNM) (US_LNM) status and tumor margin were determined as independent risk factors, which were combined for the construction of clinic_CUS prediction model for LVI in pT1 IDC. Moreover, tumor margin, US_LNM status and Radscore were independent predictors, incorporated as the radiomics nomogram model, which achieved a superior discrimination to the clinic_CUS model in the training dataset (AUC: 0.849 vs. 0.747; P < 0.001) and validation dataset (AUC: 0.854 vs. 0.713; P = 0.001). Calibration curve for the radiomic nomogram showed good concordance between predicted and actual probability. Furthermore, decision curve analysis (DCA) confirmed that the radiomics nomogram had higher clinical net benefit than the clinic_CUS model. Conclusion: The US-based radiomics nomogram, incorporating tumor margin, US_LNM status and Radscore, showed a satisfactory preoperative prediction of LVI in pT1 IDC patients.

Exceptional behavior of breast cancer-associated type 1 gene in breast invasive carcinoma.

Background: Cellular expression level of Breast Cancer-Associated Type 1 (BRCA1) encoded protein is the sign of genome integrity, stability, and surveillance. BRCA1 after sensing DNA damage activates repairing system and if mutated leaves genomic lesions unrepaired and triggers transformation of normal breast cells into cancerous ones. Aims of study: We conducted in silico study to have a holistic view of BRCA1's correlation with multiple variables of breast invasive carcinoma. Materials and Methods: We used user-friendly online GeneCardsSuite pathway-level enrichment analysis, UALCAN portal differential expression analysis, cBioPortal cancer genome platform for mutatome map construction, and cancer cell lines encyclopedia genomics of drug sensitivity toolkit to understand correlation of BRCA1 expression with the effectiveness of anti-cancer drugs. Results: Contrary to general behavior of a tumor suppressor gene our study revealed BRCA1 overexpression under all circumstances. This novel finding needs to be explored further to understand functional impact of BRCA1 overexpression on the expression of many genes which are transcriptionally regulated by BRCA1 and promotion of tumriogenesis. Conclusion: Our study highlights the potential role of BRCA1-regulated genes in oncogenesis and recommends use of BRCA1-linked genes as future therapeutic targets for effective disease management.

[Young mammary Paget's disease patients with underlying breast invasive ductal carcinoma: clinicopathological features and prognosis].

Objective: To investigate the clinicopathological factors and prognostic status of young Mammary Paget's disease (MPD) patients with invasive ductal carcinoma (IDC). Methods: In this study, we defined the age at diagnosis below 40 years old as young patients, and retrospectively analyzed data from 123 MPD-IDC patients who were admitted at the Cancer Hospital Chinese Academy of Medical Sciences from June 2002 to February 2019. Patients were divided into the young group (≤40 years old, 15 cases) and the old group (>40 years old, 108 cases) according to the age of onset, and the clinicopathological characteristics and prognosis of the two groups were compared. Cox regression model analysis was used to analyze the prognosis influencing factors. Results: The proportions of patients in the young group with non-menopausal, axillary lymph node metastasis, and Ki-67 index ≥15% were 93.3% (14/15), 73.3% (11/15), and 86.7% (13/15), respectively, which were higher than those in the old group [45.4% (49/108), 39.8%(43/108), and 60.2% (65/108), respectively] , with statistically significant differences (P<0.05). At an average follow-up of 63.2 months, patients in the young group had a significantly shorter disease-free survival (DFS) compared with that of the old group (P=0.012), while the difference in overall survival (OS) between the two groups was not statistically significant (P=0.161). Multifactorial Cox regression analysis showed that axillary lymph node status was an independent influencing factor on OS (HR=3.339, 95% CI: 1.121-9.943) in patients with MPD-IDC, while age was not. Conclusion: Compared with the old group, young patients with MPD-IDC have a higher incidence of axillary lymph node metastasis, high Ki-67 expression, and a shorter DFS, but age is not an independent influencing factor on DFS or OS in patients with MPD-IDC.

FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma.

This study investigated the prognostic value of FDG PET/CT radiomic features for predicting recurrence in patients with early breast invasive ductal carcinoma (IDC). The medical records of consecutive patients who were newly diagnosed with primary breast IDC after curative surgery were reviewed. Patients who received any neoadjuvant treatment before surgery were not included. FDG PET/CT radiomic features, such as a maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), skewness, kurtosis, entropy, and uniformity, were measured for the primary breast tumor using LIFEx software to evaluate recurrence-free survival (RFS). A total of 124 patients with early breast IDC were evaluated. Eleven patients had a recurrence (8.9%). Univariate survival analysis identified large tumor size (>2 cm, p = 0.045), high Ki-67 expression (≥30%, p = 0.017), high AJCC prognostic stage (≥II, p = 0.044), high SUVmax (≥5.0, p = 0.002), high MTV (≥3.25 mL, p = 0.044), high TLG (≥10.5, p = 0.004), and high entropy (≥3.15, p = 0.003) as significant predictors of poor RFS. After multivariate survival analysis, only high MTV (p = 0.045) was an independent prognostic predictor. Evaluation of the MTV of the primary tumor by FDG PET/CT in patients with early breast IDC provides useful prognostic information regarding recurrence.

LINC00092 Suppresses the Malignant Progression of Breast Invasive Ductal Carcinoma Through Modulating SFRP1 Expression by Sponging miR-1827.

Breast invasive ductal carcinoma (IDC) is a most common kind of breast cancer (BC), yet to date the corresponding effective therapies are limited. Extensive evidence has indicated that lncRNAs are involved in multiple cancers, and the potential mechanism of lncRNAs, such as LINC00092, mentioned in IDC remains elusive. IDC clinical samples from TCGA database were used to analyze the expression levels of LINC00092, miR-1827 and SFRP1. Kaplan-Meier method was applied to plot the overall survival curves. KEGG and GO were employed to screen the pathway that LINC00092 participated in. Pearson's correlation analysis determined the relationship between LINC00092 and SFRP1. Bioinformatics analysis and dual-luciferase reporter assay examined the association among LINC00092, miR-1827, and SFRP1. Cell counting kit-8, colony formation and transwell assays were performed to detect cell viability, colony formation, and migration and invasion, respectively. Quantitative reverse-transcription polymerase chain reaction and western blot were utilized to investigate the expression at RNA and protein levels. LINC00092 expression was down-regulated in IDC tissues and cells, which was correlated with poor prognosis. Down-regulated LINC00092 facilitated cell proliferation, colony formation, and cell migration and invasion, while up-regulated LINC00092 inhibited cell malignant behaviors. LINC00092/SFRP1 physically bound to miR-1827 in IDC. SFRP1 expression was proportional to LINC00092 expression and inversely proportional to miR-1827 expression. The inhibitory effects of LINC00092 on cell aggressive behaviors were partially regulated by miR-1827/SFRP1. In summary, our results indicated that overexpression of LINC00092 inhibited the development of IDC through modulating miR-1827/SFRP1 axis, suggesting new therapeutic targets to treat IDC.

Clinical Value of Three Combined Ultrasonography Modalities in Predicting the Risk of Metastasis to Axillary Lymph Nodes in Breast Invasive Ductal Carcinoma.

OBJECTIVE: The present study aimed to assess the clinical value of conventional ultrasound (C-US), ultrasound elastography (UE), percutaneous contrast-enhanced ultrasound (P-CUES), and the combination of these three ultrasonography modalities for evaluating the risk of axillary lymph node (ALN) metastasis in breast invasive ductal carcinoma (IDC). METHODS: This retrospective analysis included 120 patients with pathologically confirmed IDC who underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Based on the gold standard of postoperative pathology, ALN pathology results were evaluated and compared with findings obtained using C-US, UE, P-CUES, and the three modalities combined. RESULTS: (1) There was a statistically significant difference between the histological grade of the tumor and the pathological condition of ALNs. (2) The difference between C-US parameters and UE score were statistically significant. The accuracy of P-CEUS localization of SLNs was 100% (96/96) when compared with localization guided by methylene blue. The difference in the distribution of the four SLN enhancement patterns was statistically significant. (3) The sensitivity, specificity, positive predictive value, and negative predictive value of C-US and UE were 75%, 71%, 58%, and 89%, and 71%, 72%, 50%, and 86%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of P-CUES were 91%, 82%, 78%, 92%, respectively. When all three modalities were combined, the sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 89%, 86%, and 95%, respectively. In the detection of ALN metastasis, there was a good correlation between histopathological results and evaluations based on the three combined ultrasonography modalities (kappa: 0.82, p<0.001). CONCLUSIONS: When compared to C-US, UE, or P-CEUS alone, the combination of the three ultrasonography modalities was found to be superior in distinguishing metastatic and non-metastatic ALNs. This combined strategy may aid physicians in determining the most appropriate approach to ALN surgery as well as the prognosis of breast IDC.

Paravertebral block in regional anesthesia with propofol sedation reduces locoregional recurrence in patients with breast cancer receiving breast conservative surgery compared with volatile inhalational without propofol in general anesthesia.

PURPOSE: We examined locoregional recurrence (LRR) in patients with breast invasive ductal carcinoma (IDC) receiving breast conservative surgery (BCS) under propofol-based paravertebral block-regional anesthesia (PB-RA) versus sevoflurane-based inhalational general anesthesia (INHA-GA) without propofol. All-cause death and distant metastasis were secondary endpoints. PATIENTS AND METHODS: Patients with breast IDC receiving BCS were recruited through propensity score matching and categorized into INHA-GA with sevoflurane and PB-RA with propofol groups. Cox regression analysis was performed to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: In the multivariate Cox regression analysis, the adjusted HR (aHR; 95% CI) of LRR for the PB-RA with propofol group was 0.67 (0.46-0.99) compared with the INHA-GA with sevoflurane group. The aHRs of LRR for differentiation grade II, grade III, the American Joint Committee on Cancer clinical stage II, stage III, pathological tumor (pT) stage 2, pT stage 3-4, pathological nodal (pN) stage 2-3, and Her-2 positivity were 1.87 (1.03-3.42), 2.31 (1.20-4.44), 1.67 (1.09-2.56), 2.43 (1.18-4.97), 1.17 (1.03-1.19), 1.28 (1.13-2.24), 1.20 (1.05-2.22), and 1.59 (1.01-2.51), respectively, compared with those for differentiation grade I, clinical stage I, pT1, pN0, and HER-2 negativity. The aHR of LRR for adjuvant radiotherapy was 0.60 (0.38-0.97) compared with that for no adjuvant radiotherapy. CONCLUSION: PB-RA with propofol might be beneficial for reducing LRR in women with breast IDC receiving BCS compared with INHA-GA without propofol.

[Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer].

OBJECTIVE: To study the different methods of artificial intelligence (AI)-assisted Ki-67 scoring of clinical invasive ductal carcinoma (IDC) of the breast and to compare the results. METHODS: A total of 100 diagnosed IDC cases were collected, including slides of HE staining and immunohistochemical Ki-67 staining and diagnosis results. The slides were scanned and turned into whole slide image (WSI), which were then scored with AI. There were two AI scoring methods. One was fully automatic counting by AI, which used the scoring system of Ki-67 automatic diagnosis to do counting with the whole image of WSI. The second method was semi-automatic AI counting, which required manual selection of areas for counting, and then relied on an intelligent microscope to conduct automatic counting. The diagnostic results of pathologists were taken as the results of pure manual counting. Then the Ki-67 scores obtained by manual counting, semi-automatic AI counting and automatic AI counting were pairwise compared. The Ki-67 scores obtained from the manual counting (pathological diagnosis results), semi-automatic AI and automatic AI counts were pair-wise compared and classified according to three levels of difference: difference ≤10%, difference of >10%-<30% and difference ≥30%. Intra-class correlation coefficient ( ICC) was used to evaluate the correlation. RESULTS: The automatic AI counting of Ki-67 takes 5-8 minutes per case, the semi-automatic AI counting takes 2-3 minutes per case, and the manual counting takes 1-3 minutes per case. When results of the two AI counting methods were compared, the difference in Ki-67 scores was all within 10% (100% of the total), and the ICC index being 0.992. The difference between manual counting and semi-automatic AI was less than 10% in 60 cases (60% of the total), between 10% and 30% in 37 cases (37% of the total), and more than 30% in only 3 cases (3% of the total), ICC index being 0.724. When comparing automatic AI with manual counting, 78 cases (78% of the total) had a difference of ≤10%, 17 cases (17% of the total) had a difference of between 10% and 30%, and 5 cases (5%) had a difference of ≥30%, the ICC index being 0.720. The ICC values showed that there was little difference between the results of the two AI counting methods, indicating good repeatability, but the repeatability between AI counting and manual counting was not particularly ideal. CONCLUSION: AI automatic counting has the advantage of requiring less manpower, for the pathologist is involved only for the verification of the diagnosis results at the end. However, the semi-automatic method is better suited to the diagnostic habits of pathologists and has a shorter turn-over time compared with that of the fully automatic AI counting method. Furthermore, in spite of its higher repeatability, AI counting, cannot serve as a full substitute for pathologists, but should instead be viewed as a powerful auxiliary tool.

Breast invasive ductal carcinoma diagnosis with a three-miRNA panel in serum.

Aim: Breast cancer, especially invasive ductal carcinoma (IDC), is the cause of a great clinical burden. miRNA could be considered as a noninvasive biomarkers for IDC diagnosis. Materials & methods: Two hundred and sixty participants (135 IDC patients and 125 healthy controls) were enrolled in a three-cohort study. The expression of 28 miRNAs in serum were detected with quantitative reverse transcription-PCR. Bioinformatic analysis was used for predicting the target genes of three selected miRNAs. Results: The expression level of seven miRNAs (miR-9-5p, miR-34b-3p, miR-1-3p, miR-146a-5p, miR-20a-5p, miR-34a-5p, miR-125b-5p) was discrepant at the validation cohort. Through statistical test, a three-miRNA panel (miR-9-5p, miR-34b-3p, miR-146a-5p) was significant for IDC diagnosis (AUC = 0.880, sensitivity = 86.25%, specificity = 81.25%). Conclusion: The three-miRNA panel in serum could be used as a noninvasive biomarker in the diagnosis of IDC.

Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes.

BACKGROUND: Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs). METHODS: Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed. RESULTS: We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions. CONCLUSIONS: Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer.

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). FINDINGS: The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). INTERPRETATION: The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. FUNDING: NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Predicting Axillary Lymph Node Metastasis in Patients With Breast Invasive Ductal Carcinoma With Negative Axillary Ultrasound Results Using Conventional Ultrasound and Contrast-Enhanced Ultrasound.

OBJECTIVES: The purpose of this study was to establish a scoring system for predicting axillary lymph node metastasis (ALNM) in patients with breast invasive ductal carcinoma with negative axillary ultrasound (US) results. METHODS: In this retrospective study, 156 breast invasive ductal carcinoma lesions from 156 women were retrospectively enrolled. The features of conventional US and contrast-enhanced ultrasound (CEUS) qualitative enhancement patterns and quantitative enhancement parameters were analyzed. Subsequently, a scoring system was created by a multivariate logistic regression analysis. RESULTS: The results found that 60 patients (38%) showed ALNM. A scoring system was defined as risk score = 1.75 × (if lesion size ≥20 mm) + 1.93 × (if uncircumscribed margin shown on conventional US) + 1.77 × (if coarse or twisting penetrating vessels shown on CEUS). When the risk scores were less than 1.75, 1.75 to 1.93, 1.94 to 3.70, and 3.70 or higher, the risk rates of ALNM were 0% (0 of 9), 10.7% (5 of 46), 29.2% (14 of 48) and 77.4% (41 of 53), respectively. In comparison with conventional US alone, the scoring system using the combination of conventional US and CEUS showed better discrimination ability in terms of the area under the curve (0.830 versus 0.777; P = .037). CONCLUSIONS: A scoring system based on conventional US and CEUS may improve the prediction of ALNM.

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.

BACKGROUND: The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter-/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. METHODS: We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. RESULTS: The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors. CONCLUSIONS: Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.

Prognostic Significance of Metastatic Lymph Nodes Ratio (MLNR) Combined with Protein-Tyrosine Phosphatase H1 (PTPH1) Expression in Operable Breast Invasive Ductal Carcinoma.

PURPOSE: The metastatic lymph node ratio (MLNR) is one of the most important factors in prognostic analysis of breast cancer. The objective of this study was to determine if MLNR combined with protein-tyrosine phosphatase H1 (PTPH1) pathological expression can be used to predict the prognosis of patients with breast invasive ductal carcinoma (IDC) better than one factor only. PATIENTS AND METHODS: A total of 136 patients with invasive ductal carcinoma (IDC) of breast who underwent modified radical mastectomy and were treated with chemotherapy after operation at Qilu Hospital of Shandong University from December 2008 to October 2011 were included. PTPH1 expression was evaluated by immunohistochemistry in surgical specimens retrospectively collected from patients with histologically proven invasive ductal breast cancer. Kaplan-Meier survival analysis and Cox regression analysis were performed to assess the prognostic significance of PTPH1 expression. A prognostic factor for disease-free survival (DFS) was identified by univariate and multivariate analyses. ROC analysis was used to evaluate the performance of single factors and combined feature. RESULTS: One hundred and thirty-six patients were included in the analysis. By cut-point survival analysis, MLNR cut-off was designed as 0.2. On multivariate analysis, a MLNR>0.2 was associated with a worse DFS (HR=2.581, 95% CI=1.303-5.113, P=0.007). PTPH1 overexpression is correlated with a better DFS (HR=0.391, 95% CI=0.162-0.945, P=0.037). In addition, MLNR and PTPH1 combined feature had better performance in predicting clinical outcomes after surgery long before recurrence had occurred (Area under the curve=0.795 [95% CI=0.694-0.896], P<0.001). CONCLUSION: These findings indicate that both PTPH1 and MLNR are accurate independent prognostic parameters in patients with IDC of the breast. Better information on IDC prognosis could be obtained from the combined feature.