OBJECTIVE: To assess the effectiveness of a self-constructed modified apical negative pressure irrigation (ANPI) system employing commonly used clinical instruments in nonsurgical root canal therapy (NSRCT) for large cyst-like periapical lesions (LCPLs).
METHODS: From 2017 to 2022, 35 patients diagnosed with LCPLs (5-15 mm) via preoperative clinical and radiographic evaluations of endodontic origin underwent NSRCT combined with ANPI. These patients were subjected to postoperative clinical and radiographic follow-up at 3 months, 6 months, 1 year, 2 years, 3 years, and 4 years, with a CBCT scan specifically conducted at 6-month follow-up. Through the reconstruction of three-dimensional cone beam computed tomography (CBCT) data, an early prognosis was facilitated by monitoring changes in lesion volume. Various treatment predictors-including sex, type of treatment, lesion size, preoperative pain, jaw, type of teeth involved, sealer extrusion, and the number of root canals-were meticulously analyzed. The evaluation of post-treatment outcomes leveraged both clinical observations and radiographic data collected during the follow-up periods. The Kruskal‒Wallis test and one-way ANOVA were also conducted to determine the independent factors influencing treatment outcomes. A significance level of 5% was established.
RESULTS: Thirty-five teeth from 35 patients with a median age of 28 years (range 24-34) were treated; the median follow-up duration was 19 months (range 12-26). The overall success rate was 91.4%, with a median lesion reduction of 77.0% (range 54.2-96.4%) at 6 months. Patients under 30 years of age exhibited a significantly greater success rate than older patients did (100.0% vs. 80.0%, p = 0.037). Other factors, such as sex, jaw, treatment type, preoperative pain, cyst size, tooth location, sealer extrusion, and the number of roots, did not significantly impact treatment outcomes.
CONCLUSIONS: Despite limitations related to the observational case-series study design and relatively small sample size, our findings suggest that utilizing the ANPI in the NSRCT for LCPLs may hold promise. The notably higher success rate in patients younger than 30 years is worth noting.

The optimal number of cycles of induction chemotherapy (IC) in locoregionally advanced nasopharyngeal carcinoma (LANPC) remains unresolved. This study aimed to quantitatively assess the changes in gross tumor volumes (GTVs) and to select the most optimal number of IC cycles.
We analyzed 54 patients who received a three-cycle IC before commencing radiotherapy, with the tumor and nodal responses assessed by a CT scan before IC and after each IC cycle. The gross tumor volumes of the nasopharynx primary lesion (GTV_T), involved retropharyngeal lymph node (GTV_RP), and involved cervical lymph node (GTV_N) were contoured on each scan. The volume change following each IC cycle was evaluated with Wilcoxon signed-rank test. The three-dimensional vector displacements of target centers were also calculated and compared.
The volume reductions of GTVs following IC varied across different patients and showed different trends for the three GTV types. GTV_T and GTV_RP did not display further volume reduction after two IC cycles, whereas GTV_N showed monotonic volume decreases. For GTV_T and GTV_RP following the three IC cycles, the total volume reduction relative to the initial volume before IC was 12.0%, 22.5%, and 20.1% and 26.0%, 44.1%, and 42.2%, respectively. In contrast, for GTV_N, continuing volume reduction was observed with a total reduction of 25.3%, 43.2%, and 54.7% following the three cycles, and the reductions were all significant. Average displacements of the GTVs were <1.5 mm in all directions; their average three-dimensional displacements were 2.6, 4.0, and 1.7 mm, respectively. Acceptable toxicity was observed in most patients.
This study supports two cycles of IC before radiotherapy for patients with LANPC if the initial metastatic cervical lymph node volume is not dominating. Otherwise, three cycles of IC is recommended to further reduce the cervical node volume.

Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.