Comparison of postoperative CT- and preoperative MRI-based breast tumor bed contours in prone position for radiotherapy after breast-conserving surgery

A Contrast-Enhanced Tri-Modal MRI Technique for High-Performance Hypoxia Imaging of Breast Cancer

Personalized radiotherapy strategies enabled by the construction of hypoxia-guided biological target volumes (BTVs) can overcome hypoxia-induced radioresistance by delivering high-dose radiotherapy to targeted hypoxic areas of the tumor. However, the construction of hypoxia-guided BTVs is difficult owing to lack of precise visualization of hypoxic areas. This study synthesizes a hypoxia-responsive T1, T2, T2 mapping tri-modal MRI molecular nanoprobe (SPION@ND) and provides precise imaging of hypoxic tumor areas by utilizing the advantageous features of tri-modal magnetic resonance imaging (MRI). SPION@ND exhibits hypoxia-triggered dispersion-aggregation structural transformation. Dispersed SPION@ND can be used for routine clinical BTV construction using T1-contrast MRI. Conversely, aggregated SPION@ND can be used for tumor hypoxia imaging assessment using T2-contrast MRI. Moreover, by introducing T2 mapping, this work designs a novel method (adjustable threshold-based hypoxia assessment) for the precise assessment of tumor hypoxia confidence area and hypoxia level. Eventually this work successfully obtains hypoxia tumor target and accurates hypoxia tumor target, and achieves a one-stop hypoxia-guided BTV construction. Compared to the positron emission tomography-based hypoxia assessment, SPION@ND provides a new method that allows safe and convenient imaging of hypoxic tumor areas in clinical settings.

Target volumes comparison between postoperative simulation magnetic resonance imaging and preoperative diagnostic magnetic resonance imaging for prone breast radiotherapy after breast-conserving surgery

BACKGROUND

This study investigated the differences in target volumes between preoperative magnetic resonance imaging (MRIpre) and postoperative MRI (MRIpost) for breast radiotherapy after breast-conserving surgery (BCS) using deformable image registration (DIR).

METHODS AND MATERIALS

Seventeen eligible patients who underwent whole-breast irradiation in the prone position after BCS were enrolled. On MRIpre, the gross tumor volume (GTV) was delineated as GTVpre, which was then expanded by 10 mm to represent the preoperative lumpectomy cavity (LC), denoted as LCpre. The LC was expanded to the clinical target volume (CTV) and planning target volume (PTV) on the MRIpre and MRIpost, denoted as CTVpre, CTVpost, PTVpre, and PTVpost, respectively. The MIM software system was used to register the MRIpre and MRIpost using DIR. Differences were evaluated regarding target volume, distance between the centers of mass (dCOM), conformity index (CI), and degree of inclusion (DI). The relationship between CILC /CIPTV and the clinical factors was also assessed.

RESULTS

Significant differences were observed in LC and PTV volumes between MRIpre and MRIpost (p < 0.0001). LCpre was 0.85 cm3 larger than LCpost, while PTVpre was 29.38 cm3 smaller than PTVpost. The dCOM between LCpre and LCpost was 1.371 cm, while that between PTVpre and PTVpost reduced to 1.348 cm. There were statistically significant increases in CI and DI for LCpost-LCpre and PTVpost-PTVpre (CI = 0.221, 0.470; DI = 0.472, 0.635). No obvious linear correlations (p > 0.05) were found between CI and GTV, primary tumor volume-to-breast volume ratio, distance from the primary tumor to the nipple and chest wall, and body mass index.

CONCLUSIONS

Despite using DIR technology, the spatial correspondence of target volumes between MRIpre and MRIpost was suboptimal. Therefore, relying solely on preoperative diagnostic MRI with DIR for postoperative LC delineation is not recommended.

The King is in the altogether: Radiation therapy after oncoplastic breast surgery

Breast cancer is the most common malignancy, and the majority of the patients are diagnosed at an early disease stage. Breast conservation is the preferred locoregional approach, and oncoplastic breast conservation surgery is becoming more popular. This narrative review aims to discuss the challenges and uncertainties in target volume definition for postoperative radiation after these procedures, to improve radiation therapy decisions and encourage multidisciplinary.

Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique

Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.