Jiayin Zhou's research while affiliated with Fudan University and other places

Background and Aim The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple‐negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m‐IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m‐IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. Methods The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD‐L1, and Pan‐CK markers were stained by m‐IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. Results The components of the TIME score were established by m‐IHC and demonstrated positive prognostic value for TNBC ( p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD‐L1 ⁺ cell% in the SA, CD3 + cell% in the TC, and PD‐L1 ⁺ /CD8 ⁺ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. Conclusion We demonstrated that the m‐IHC‐based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer‐related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

Background: The study aimed to evaluate the role of digital breast tomosynthesis (DBT) in the differential diagnosis of digital mammography (DM) lesions classified as 4A according to the Breast Imaging Reporting and Data System (BI-RADS). Methods: The DM and DBT of patients diagnosed with non-calcified BI-RADS 4A lesions at our institution between January 2019 and August 2020 were analyzed retrospectively. The BI-RADS 4A lesions on DM were downgraded to BI-RADS 3 if the lesion on DBT was more visible than on the mammogram and more than 50% of its boundary was sharp without suspicious signs of malignancy. Conversely, the lesions were upgraded to BI-RADS 4B and 4C if they presented with an irregular shape or showed non-circumscribed margins on DBT. The Mann-Whitney U test was used to compare the categorical variables, and the T-test was used to analyze the continuous variables between the benign and malignant pathologically confirmed cases. Results: A total of 191 lesions were evaluated, of which 129 were confirmed to be benign on pathology examination, and the rest were malignant.After plus DBT,25.1% of lesions showed lesion type change which was asymmetry on DM but presented mass or architectural distortion on DBT, and 46.3 % of the asymmetric DM lesions were seen as a uniform mass on DBT. DBT showed superior or equal visualization to DM for circumscribed masses. Most (97.2%) of the circumscribed masses on DBT were confirmed benign on pathology, while 91.7% of the non-circumscribed were confirmed to be malignant on pathology. After plus DBT,61.8% of the lesions had BI-RADS change, for the lesions downgraded to BI-RADS 3 on DBT, there were 54.3% confirmed to be benign on pathology, while 64.5% of upgraded to BI-RADS 4B or 4C lesions were malignant on pathology. Conclusion: DBT can be used to facilitate the discrimination of benign and malignant BI-RADS 4A lesions on DM and hence reduce the need for unnecessary biopsies.

Background: The upgrade of high-risk breast lesions (HRLs) is closely related to subsequent treatment, but the current predictors for upgrade are limited to intratumoral features of single imaging mode. Methods: We retrospectively reviewed 230 HRLs detected by mammography, ultrasound, and magnetic resonance imaging (MRI) before biopsy at the Fudan University Cancer Hospital from January 2017 to March 2018. The clinical features, imaging data according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon, and tumor upgrade situation were received. Based on the different risks of upgrade reported, the lesions were classified into high-risk I [HR-I, with atypical hyperplasia (AH)] and high-risk II (HR-II, without AH). We analyzed the association between clinicopathological and imaging factors and upgrade. We used the receiver operating characteristic (ROC) curve to compare the efficacy of three imaging modes for predicting upgrade. Results: We included 230 HRLs in 230 women in the study, and the overall upgrade rate was 20.4% (47/230). The upgrade rate was higher in HR-I compared to HR-II (38.5% vs. 4.1%, P<0.01). In patients with AH, estrogen receptor-positive (ER+) patients accounted for 81.0% (64/79). For all HRLs and HR-I, in clinical characteristics, age, maximum size of lesion, and menopausal status were significantly associated with upgrade (P<0.05). In imaging factors, MRI background parenchymal enhancement (BPE), signs of MRI and ultrasound were significantly correlated with upgrade (P<0.05). Patients with negative MRI or ultrasound manifestations had lower upgrade rates (P<0.01). For HR-II, only BPE showed a significant difference between groups (P=0.001). Multifactorial analysis of all HRLs showed that age and BPE were independent predictors of upgrade (P<0.01). The areas under the ROC cure (AUCs) for predicting upgrade in mammography, ultrasound, and MRI were 0.606, 0.590, and 0.913, respectively, indicating that MRI diagnosis was significantly better than mammography and ultrasound (P<0.001). Conclusions: HRLs with AH had a higher rate of upgrade and increased ER expression. Among three imaging modes, MRI was more effective than ultrasound and mammography in diagnosing the upgrade of HRLs. Older age and moderate to marked BPE can indicate malignant upgrade. MRI can provide a certain value for the diagnosis and follow-up of HRLs.