MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.

BACKGROUND: The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume. MATERIAL AND METHODS: A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step. RESULTS: The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists. CONCLUSION: The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses. NO LEVEL ASSIGNED: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Correlation of IDH1 gene expression error in breast tumor biopsy in patients with invasive ductal carcinoma.

One of the most important cancers in terms of worldwide prevalence is breast tumors, which have been less investigated in correlation with the enzyme Isocitrate Dehydrogenase 1 (IDH1) gene. The aim of this study was that expression of this gene could have significant effects on the progression of metastasis and invasive disease in breast cancer patients. We used the molecular method of RT-PCR with SYBR-Green to analyze breast tumor tissue from patients with metastasis and non-metastasis, the latter confirmed by the pathology department of Shohada-e Tajrish Hospital (serving as a control group). Also, patients population and its relationship with the degree of tumor in the IDH1 gene was investigated. The IDH1 gene has shown high expression in patients with metastatic breast cancer rather than in patients with non-metastatic breast cancer. The metastatic samples were compared with non-metastatic samples for IDH1 mRNA expression. In this research work, 72.5% (29 samples) were up-regulated in comparison to 27.5% of samples (11 samples) that did not exhibit high expression (P=0.000).  This study examined the IDH1 gene expression, suggesting that changes in this gene's expression could impact the prognosis of breast cancer. However, further research is needed to draw definitive conclusions.

Radiation effects, zero thermal expansion, and pressure-induced phase transition in CsMnCo(CN)6.

The Prussian blue analogue CsMnCo(CN)6 is studied using powder X-ray and neutron diffraction under variable temperature, pressure, and X-ray exposure. It retains cubic F4̄3m symmetry in the range 85-500 K with minimal thermal expansion, whereas a phase transition to P4̄n2 occurs at ∼2 GPa, driven by octahedral tilting. A small lattice contraction occurs upon increased X-ray dose. Comparisons with related systems indicate that the CsI ions decrease the thermal expansion and suppress the likelihood of phase transformations. The results improve the understanding of the stimuli-responsive behaviour of coordination polymers.

Multiple datasets to explore the tumor microenvironment of cutaneous squamous cell carcinoma.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) is one of the most frequent types of cutaneous cancer. The composition and heterogeneity of the tumor microenvironment significantly impact patient prognosis and the ability to practice precision therapy. However, no research has been conducted to examine the design of the tumor microenvironment and its interactions with cSCC. MATERIAL AND METHODS: We retrieved the datasets GSE42677 and GSE45164 from the GEO public database, integrated them, and analyzed them using the SVA method. We then screened the core genes using the WGCNA network and LASSO regression and checked the model's stability using the ROC curve. Finally, we performed enrichment and correlation analyses on the core genes. RESULTS: We identified four genes as core cSCC genes: DTYMK, CDCA8, PTTG1 and MAD2L1, and discovered that RORA, RORB and RORC were the primary regulators in the gene set. The GO semantic similarity analysis results indicated that CDCA8 and PTTG1 were the two most essential genes among the four core genes. The results of correlation analysis demonstrated that PTTG1 and HLA-DMA, CDCA8 and HLA-DQB2 were significantly correlated. CONCLUSIONS: Examining the expression levels of four primary genes in cSCC aids in our understanding of the disease's pathophysiology. Additionally, the core genes were found to be highly related with immune regulatory genes, suggesting novel avenues for cSCC prevention and treatment.

A Bayesian Network Meta-Analysis of Complications Related to Breast Reconstruction Using Different Skin Flaps After Breast Cancer Surgery.

BACKGROUND AND OBJECTIVES: As the incidence of breast cancer rises, the number of mastectomy surgeries surges, so does the importance of postoperative breast reconstruction. The implementation of autologous flap restoration methods is becoming prevalent, although which is the best flap remains controversial. As a result, we performed a Bayesian network meta-analysis to compare the eight most common flap in the reconstruction processor of breast cancer surgery. Our findings may help surgeons decide which skin flaps to use for breast reconstruction. METHODS: We searched PubMed, Medline, Embase, and the Cochrane library for relevant literature. For our Bayesian network meta-analysis, we scrutinized 37 papers and evaluated the postoperative complications of eight commonly used breast reconstruction procedures. We also registered this study on PROSPERO, with the number CRD42021251989. RESULTS: A total of 21,184 patients were included in this Bayesian network meta-analysis from 37 different studies. The results demonstrate that TRAM flaps are more prone to complications such as hernias in the abdominal wall and blood flow problems. Hematoma and seroma are more likely to follow LDP flaps. Combining LDP flaps with a prosthetic or autologous adipose tissue does not enhance the risk of postoperative problems appreciably. Fat liquefaction are relatively common in DIEP. CONCLUSIONS: After breast reconstruction, several skin flaps can be employed as clinical choices. TRAM flaps are not recommended for patients with a weak abdominal wall structure, although LDP flaps or SIEA flaps can be considered instead. We do not advocate LDP flaps for patients who have had breast surgery because of the higher risk of hematoma or seroma, but DIEP flaps or LAP flaps can be utilized instead. We do not propose DIEP flaps for individuals who are at a higher risk of postoperative fat liquefaction, but LDP flaps or SIEA flaps can be used instead. However, this Bayesian network meta-analysis has limitations, and further randomized controlled trials are needed to confirm its findings. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

KLF9 (Kruppel Like Factor 9) induced PFKFB3 (6-Phosphofructo-2-Kinase/Fructose-2, 6-Biphosphatase 3) downregulation inhibits the proliferation, metastasis and aerobic glycolysis of cutaneous squamous cell carcinoma cells.

Cutaneous squamous cell carcinoma (CSCC) is the second most common skin cancer in humans with increasing incidence. In this paper, we focused on the effects of krueppel-like factor 9 (KLF9) on the progression of CSCC cells by binding to PFKFB3. mRNA and protein expressions of KLF9 and PFKFB3 in human HaCaT and CSCC cells were, respectively, examined by RT-qPCR analysis and Western blot. The viability, proliferation, invasion and migration of A431 cells after transfection were analyzed with MTT, clone formation, transwell and wound healing assays. The levels of glucose, lactic acid and ATP in transfected A431 cells were detected by their commercial kits. Ki-67 expression in transfected A431 cells was determined using immunofluorescence analysis and in tumor tissues was analyzed by immunohistochemistry. The levels of migration, EMT and aerobic glycolysis-related proteins were tested with Western blot. The combination of KLF9 and PFKFB3 was confirmed by dual-luciferase reporter assay and ChIP. As a result, PFKFB3 expression was elevated in CSCC cells compared with HaCaT. Knockdown of PFKFB3 restrained the proliferation, metastasis, and aerobic glycolysis of CSCC cells. In addition, KLF9 could bind to PFKFB3. Downregulation of KLF9 crippled the inhibitory effect of knockdown of PFKFB3 on the proliferation, metastasis, and aerobic glycolysis of CSCC cells. In conclusion, PFKFB3 was transcriptionally regulated by KLF9, and PFKFB3 silencing inhibits the proliferation, metastasis, and aerobic glycolysis of cutaneous squamous cell carcinoma cells.

A two-dimensional molecular beacon for mRNA-activated intelligent cancer theranostics.

Ideal theranostics should possess directly correlated imaging and therapy modalities that could be simultaneously activated in the disease site to generate high imaging contrast and therapeutic efficacy with minimal side effects. However, so far it still remains challenging to engineer all these characteristics into a single theranostic probe. Herein, we report a new type of photosensitizer (PS)-derived "two-dimensional" molecular beacon (TMB) that could be specifically activated within tumor cells to exhibit both high imaging contrast and therapeutic efficacy that outperforms conventional photosensitizers for cancer theranostics. The TMB is constructed by integrating a photosensitizer (chlorin e6 (Ce6)), a quantum dot (QD), and a dark quencher (BHQ3) into a hairpin DNA molecule to generate multiple synergistic FRET modes. The imaging modality and therapy modality, which are mediated by FRET between the QD and BHQ3 and FRET between the QD and Ce6 respectively, are interconnected within the TMB and could be simultaneously activated by tumor mRNA molecules. We show that highly effective cancer imaging and therapy could be achieved for cancer cell lines and xenografted tumor models. The reported TMB represents an unprecedented theranostic platform for intelligent cancer theranostics.