Low breast density and peritumoral edema on MR predict worse overall survival of breast cancer patients after neoadjuvant chemotherapy.

OBJECTIVES: To investigate the relationship of pre-treatment MR image features (including breast density) and clinical-pathologic characteristics with overall survival (OS) in breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: This retrospective study obtained an approval of the institutional review board and the written informed consents of patients were waived. From October 2013 to April 2019, 130 patients (mean age, 47.6 ± 9.4 years) were included. The univariable and multivariable Cox proportional hazards regression models were applied to analyze factors associated with OS, including MR image parameters and clinical-pathologic characteristics. RESULTS: Among the 130 included patients, 11 (8.5%) patients (mean age, 48.4 ± 11.8 years) died of breast cancer recurrence or distant metastasis. The median follow-up length was 70 months (interquartile range of 60-85 months). According to the Cox regression analysis, older age (hazard ratio [HR] = 1.769, 95% confidence interval [CI]): 1.330, 2.535), higher T stage (HR = 2.490, 95%CI:2.047, 3.029), higher N stage (HR = 1.869, 95%CI:1.507, 2.317), low breast density (HR = 1.693, 95%CI:1.391, 2.060), peritumoral edema (HR = 1.408, 95%CI:1.078, 1.840), axillary lymph nodes invasion (HR = 3.118, 95%CI:2.505, 3.881) on MR were associated with worse OS (all p < 0.05). CONCLUSIONS: Pre-treatment MR features and clinical-pathologic parameters are valuable for predicting long-time OS of breast cancer patients after NAC followed by surgery. Low breast density, peritumoral edema and axillary lymph nodes invasion on pre-treatment MR images were associated with worse prognosis.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence.

Background: In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma. Methods: This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence. Results: The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440). Conclusions: The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment.

BACKGROUND: Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable. PURPOSE: To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images. STUDY TYPE: Retrospective. SUBJECTS: A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results. FIELD STRENGTH/SEQUENCE: A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence. ASSESSMENT: In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant. RESULTS: For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635. DATA CONCLUSION: Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Flexible human serum albumin nanocapsules to enhance drug delivery and cellular uptake for photodynamic/chemo cancer therapy.

As a non-invasive cancer treatment, photodynamic therapy (PDT) has great applications in superficial tumors because of its high selectivity and low cumulative toxicity. However, the poor tumor-targeting ability and short blood circulation time of conventional photosensitizers (PSs) limit the efficacy of PDT to some extent. In this study, we synthesized flexible hollow human serum albumin (HHSA) and loaded photosensitizer Chlorin e6 (Ce6) and the chemotherapeutic drug Doxorubicin (DOX) for synergistic cancer therapy. HHSA can enhance drug delivery and cellular uptake through targeting gp60 and SPARC receptors and unique flexible hollow structures. The TEM images show that HHSA possesses distinct flexible hollow structures, as well as good monodispersity and deformability. After loading Ce6 and DOX, HHSA@Ce6-DOX displays better therapeutic effects than HHSA@DOX on the growth of 4T1 breast cancers without irradiation. Remarkably, it has a significantly higher therapeutic effect (relative cell activity: 45% vs. 74%) than HHSA@Ce6 under 660 nm irradiation. Furthermore, the excellent biocompatibility of HHSA@Ce6-DOX has been proved both in vitro and in vivo, indicating that it has a promising future in synergistic tumor treatments.

Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses.

PURPOSE: We aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI). METHODS: In this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses. RESULTS: The routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis. CONCLUSIONS: Our study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.

Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice (SMS) technique for diagnosing breast lesions.

OBJECTIVES: To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions. METHODS: This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis. RESULTS: Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05). CONCLUSIONS: Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI. KEY POINTS: • SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

Association of "initial CT" findings with mortality in older patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES: To investigate the association of chest CT findings with mortality in clinical management of older patients. METHODS: From January 21 to February 14, 2020, 98 older patients (≥ 60 years) who had undergone chest CT scans ("initial CT") on admission were enrolled. Manifestation and CT score were compared between the death group and the survival group. In each group, patients were sub-grouped based on the time interval between symptom onset and the "initial CT" scan: subgroup1 (interval ≤ 5 days), subgroup2 (interval between 6 and 10 days), and subgroup3 (interval > 10 days). Adjusted ROC curve after adjustment for age and gender was applied. RESULTS: Consolidations on CT images were more common in the death group (n = 46) than in the survival group (n = 52) (53.2% vs 32.0%, p < 0.001). For subgroup1 and subgroup2, a higher mean CT score was found for the death group (33.0 ± 17.1 vs 12.9 ± 8.7, p < 0.001; 38.8 ± 12.3 vs 24.3 ± 11.9, p = 0.002, respectively) and no significant difference of CT score was identified with respect to subgroup3 (p = 0.144). In subgroup1, CT score of 14.5 with a sensitivity of 83.3% and a specificity of 77.3% for the prediction of mortality was an optimal cutoff value, with an adjusted AUC of 0.881. In subgroup2, CT score of 27.5 with a sensitivity of 87.5% and a specificity of 70.6% for the prediction of mortality was an optimal cutoff value, with an adjusted AUC of 0.895. CONCLUSIONS: "Initial CT" scores may be useful to speculate prognosis and stratify patients. Severe manifestation on CT at an early stage may indicate poor prognosis for older patients with COVID-19. KEY POINTS: • Severe manifestation on CT at an early stage may indicate poor prognosis for older patients with COVID-19. • Radiologists should pay attention to the time interval between symptom onsets and CT scans of patients with COVID-19. • Consolidations on CT images were more common in death patients than in survival patients.

Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.

Background Chest CT is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with RT-PCR assay in COVID-19. Materials and Methods This study included 1014 patients in Wuhan, China, who underwent both chest CT and RT-PCR tests between January 6 and February 6, 2020. With use of RT-PCR as the reference standard, the performance of chest CT in the diagnosis of COVID-19 was assessed. In addition, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative) was analyzed as compared with serial chest CT scans for those with a time interval between RT-PCR tests of 4 days or more. Results Of the 1014 patients, 601 of 1014 (59%) had positive RT-PCR results and 888 of 1014 (88%) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95% confidence interval: 95%, 98%; 580 of 601 patients) based on positive RT-PCR results. In the 413 patients with negative RT-PCR results, 308 of 413 (75%) had positive chest CT findings. Of those 308 patients, 48% (103 of 308) were considered as highly likely cases and 33% (103 of 308) as probable cases. At analysis of serial RT-PCR assays and CT scans, the mean interval between the initial negative to positive RT-PCR results was 5.1 days ± 1.5; the mean interval between initial positive to subsequent negative RT-PCR results was 6.9 days ± 2.3. Of the 1014 patients, 60% (34 of 57) to 93% (14 of 15) had initial positive CT scans consistent with COVID-19 before (or parallel to) the initial positive RT-PCR results. Twenty-four of 57 patients (42%) showed improvement on follow-up chest CT scans before the RT-PCR results turned negative. Conclusion Chest CT has a high sensitivity for diagnosis of coronavirus disease 2019 (COVID-19). Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Smart Bacterial Magnetic Nanoparticles for Tumor-Targeting Magnetic Resonance Imaging of HER2-Positive Breast Cancers.

Supersensitive magnetic resonance (MR) imaging requires contrast with extremely high r2 values. However, synthesized magnetic nanoparticles generally have a relatively low r2 relaxivity. Magnetosomes with high saturation magnetization and good biocompatibility have shown potential values as MR imaging contrast agents. Magnetosomes that target human epidermal growth factor receptor-2 (HER2) were prepared using genetic technology and low-frequency sonication. Anti-HER2 affibody of the ability to target HER2 was displayed on the membrane surface of the magnetosomes through the anchor protein MamC, allowing the bacterial nanoparticles to target tumors overexpressing HER2. The prepared nanoparticles exhibited a very high relaxivity of 599.74 mM-1 s-1 and better dispersion, and their ability to target HER2 was demonstrated both in vitro and in vivo. Also, the HER2-targeting magnetosomes significantly enhanced the MR imaging of orthotopic breast cancer models with or without HER2 expression using a 7.0 T scanner. In particular, tumors overexpressing HER2 demonstrated better MR imaging than HER2-negative tumors after intravenous administration of HER2-targeting magnetosomes, and the MR signals of the augmented contrast could be detected from 3 to 24 h. The magnetosomes did not cause any notable pathogenic effect in the animals. Therefore, we expect that noninvasive imaging of tumors using HER2-targeting magnetosomes has potential for clinical applications in the near future.

Sensitive, Real-Time, and In-Vivo Oxygen Monitoring for Photodynamic Therapy by Multifunctional Mesoporous Nanosensors.

Real-time monitoring of oxygen consumption is beneficial to predict treatment responses and optimize therapeutic protocols for photodynamic therapy (PDT). In this work, we first demonstrate that deformable hollow mesoporous organosilica nanoparticles (HMONs) can be used to load [(Ru(dpp)3)]Cl2 for detecting oxygen (denoted as HMON-[(Ru(dpp)3)]Cl2). This nanoprobe shows significantly improved biocompatibility and high cellular uptake. In-vitro experiments demonstrate that the HMON-[(Ru(dpp)3)]Cl2 can sensitively detect oxygen changes between 1% and 20%. On this basis, photosensitizer chlorin e6 (Ce6) and [(Ru(dpp)3)]Cl2 are simultaneously loaded in the HMONs (denoted as HMON-Ce6-[(Ru(dpp)3)]Cl2) for real-time oxygen monitoring during photodynamic therapy. The HMON-Ce6-[(Ru(dpp)3)]Cl2 can reflects oxygen consumption in solution and cells in photodynamic therapy. Furthermore, the ability of the HMON-Ce6-[(Ru(dpp)3)]Cl2 nanosensor to monitor oxygen changes is demonstrated in tumor-bearing nude mice.

Anti-EGFR Peptide-Conjugated Triangular Gold Nanoplates for Computed Tomography/Photoacoustic Imaging-Guided Photothermal Therapy of Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is difficult to cure because of the high recurrence rate and the side effects of current treatments. It is urgent to develop a new treatment that is safer and more effective than current treatments against NSCLC. Herein, we constructed anti-epidermal growth factor receptor (EGFR) peptide-conjugated PEGylated triangular gold nanoplates (TGN-PEG-P75) as a targeting photothermal therapy (PTT) agent to treat NSCLC under the guidance of computed tomography (CT) and photoacoustic (PA) imaging. The surface of TGNs is successfully conjugated with a novel peptide P75 that has the specific affinity to epidermal growth factor receptor (EGFR). It is found that the EGFR is overexpressed in NSCLC cells. The TGN-PEG-P75 has uniform edge length (77.9 ± 7.0 nm) and neutrally charged surface. The cell uptake experiments demonstrate remarkable affinity of the TGN-PEG-P75 to high EGFR expression cells than low EGFR expression cells (5.1-fold). Thanks to the strong near-infrared absorbance, high photothermal conversion efficiency, and the increased accumulation in tumor cells via the interaction of P75 and EGFR, TGN-PEG-P75 exhibits 3.8-fold superior therapeutic efficacy on HCC827 cells than TGN-PEG. The in vivo CT/PA dual-modal imaging of the TGN-PEG-P75 is helpful in selecting the optimal treatment time and providing real-time visual guidance of PTT. Furthermore, treatments on HCC827 tumor-bearing mouse model demonstrate that the growth of NSCLC cells can be effectively inhibited by the TGN-PEG-P75 through PTT, indicating the great promise of the nanoplatform for treating NSCLC in vivo.

Tumor Acidic Microenvironment Targeted Drug Delivery Based on pHLIP-Modified Mesoporous Organosilica Nanoparticles.

Enhancing the tumor-targeting delivery of chemotherapeutic drugs is important yet challenging for improving therapeutic efficacy and reducing the side effects. Here, we first construct a drug delivery system for targeting tumor acidic microenvironment by modification of pH (low) insertion peptide (pHLIP) on mesoporous organosilica nanoparticles (MONs). The MONs has thioether-bridged framework, uniform diameter (60 nm), good biocompatibility, and high doxorubicin (DOX) loading capacity (334 mg/g). The DOX loaded in the pHLIP modified MONs can be released responsive to glutathione and low pH circumstance, ensuring the chemotherapeutic drug exerts higher cytotoxic effects to cancer cells than normal cells because of high intracellular GSH of tumor cells and low pH of tumor microenvironment. Moreover, the engineered MONs exhibit higher cellular uptake in pH 6.5 medium by MDA-MB-231 and MCF-7 cells than the particles decorated with polyethylene glycol (PEG). Importantly, the pHLIP-mosaic MONs with DOX displays better cytotoxic effects against the breast cancer cells in pH 6.5 medium than pH 7.4 medium. The in vivo experiments demonstrate that the pHLIP modified MONs are accumulated in the orthotopic breast cancer via targeting to acidic tumor microenvironment while no serious pathogenic effects was observed. After loading DOX, the pHLIP-modified MONs display better therapeutic effects than the control groups on the growth of MCF-7 breast cancers, showing promise for enhancing chemotherapy.

Gold nanorod embedded large-pore mesoporous organosilica nanospheres for gene and photothermal cooperative therapy of triple negative breast cancer.

To date, clinicians still lack an effective strategy to treat triple negative breast cancer (TNBC). In this work, we design for the first time a gold nanorod embedded large-pore mesoporous organosilica (GNR@LPMO) nanoplatform for gene and photothermal cooperative therapy of TNBC. The synthesized GNR@LPMOs possess a uniform size (175 nm), high surface area (631 m2 g-1), large pore size, excellent photothermal efficiency, and good biocompatibility. Thanks to the large-pore mesoporous organosilica layer, the GNR@LPMO nanoplatforms display much higher loading capacity of siRNA compared with traditional liposome and bare gold nanorods. Thus, functional siRNA can be efficiently delivered into TNBC cells by GNR@LPMOs, causing much higher cell apoptosis through knocking down the PLK1 proteins. By combining the effective gene delivery and photothermal abilities, the GNR@LPMO nanoplatforms are further used for gene and photothermal cooperative therapy of TNBC, which induce a 15 fold higher mice tumor inhibition rate than sole therapy modality, indicating the potential clinical use of this novel nanoplatform in treating TNBC.