To evaluate the performance of habitat analysis by positron emission tomography (PET)/computed tomography (CT) with ¹⁸F-prostate-specific membrane antigen (PSMA)-1007 (¹⁸F-PSMA-1007 PET/CT) for prediction of risk grading based on the Gleason Score (GS) for primary prostate cancer (PCa).

The data of 42 PCa patients who underwent ¹⁸F-PSMA-1007 PET/CT before puncture biopsy or radical prostatectomy were included for analysis. The whole prostate was manually contoured on PET and CT images as the volume of interest (VOI). Using the Otsu algorithm, the VOI was divided into four habitat subregions. Independent risk factors were screened and a combined model was constructed to predict GS grade by univariate logistic regression followed by multivariate logistic regression of habitat (1-4) and clinical factors (SUV_max, tPSA, fPSA/tPSA, age). Receiver operating characteristic (ROC) curves were drawn and the area under the ROC curve (AUC), sensitivity, and specificity were calculated to evaluate indicator performance. The Kappa consistency test was used to evaluate the agreement between predictive indicators and the model with pathological results. DeLong's test was used to compare the AUC values.

SUV_max (OR, 1.139; 95% CI, 1.034-1.253; p = 0.008) and the Habitat 2 spatial proportion (OR, 1.166; 95% CI, 1.041-1.307; p = 0.008) were identified by logistic regression analysis as independent risk factors to distinguish the GS grading of PCa, which the Habitat 2 spatial proportion represented the percentage of voxels in the region with PET-high uptake and CT-low density to the VOI. The AUC values of SUV_max, Habitat 2 spatial proportion, and the combined prediction model were 0.750 (95% CI, 0.597-0.903), 0.716 (95% CI, 0.559-0.873), and 0.823 (95% CI, 0.694-0.951), respectively. The sensitivity of Habitat 2 spatial proportion was 90.91%, which was higher than SUV_max (72.73%) and the combined model (68.18%). The specificity of the model combining SUV_max and Habitat 2 spatial proportion for risk classification of PCa was 90.00%, which was higher than either SUV_max (75.00%) or Habitat 2 spatial proportion (45.00%).

The results of this pilot study showed that the combined prediction model, as a non-invasive method, may provide additional value for risk stratification of PCa, offering new perspectives for individualized clinical diagnosis and treatment.

https://www.chictr.org.cn/ .

Registration number: ChiCTR2100052238 (retrospectively registered).

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related deaths in women worldwide. Approximately 20-30% of women diagnosed with early-stage breast cancer eventually develop metastatic disease. Current biomarkers, such as CA15-3 and CEA, detect metastasis in only 60-80% of cases, underscoring the need for improved diagnostic tools. This study investigates the potential of circulating methylated GCM2 and TMEM240 as biomarkers for noninvasive monitoring of breast cancer progression.

In a prospective study conducted in Taiwan, 396 patients were enrolled, alongside a retrospective study of 134 plasma samples from Western populations. cfDNA was extracted, subjected to sodium bisulfite conversion, and the methylation levels of GCM2 and TMEM240 were measured using QMSP. Monte Carlo analysis assigned 70% of the dataset to a training set and 30% to a validation set, repeated 1000 times. Performance metrics such as sensitivity, specificity, and accuracy were averaged to ensure robustness, supporting the use of combined GCM2 and TMEM240 for monitoring treatment response and tumor burden.

The training set, consisting of 166 breast cancer patients (13.3% with recurrence or metastasis), was utilized to establish the biomarker detection cutoff. Validation in a separate cohort of 325 patients (20% with recurrence or metastasis) demonstrated superior performance compared to CA15-3 and CEA, achieving 95.1% accuracy, 89.4% sensitivity, 96.5% specificity, 86.8% positive predictive value (PPV), and 97.3% negative predictive value (NPV). Monte Carlo analysis of the training data revealed an average sensitivity of 95.7%, specificity of 90.3%, and accuracy of 91.5%, while validation data achieved 92.8% sensitivity, 89.5% specificity, and 90.3% accuracy across 1000 replicates. Positive cases were significantly associated with late-stage disease (P < 0.001), larger tumors (P = 0.002), distant metastasis (P < 0.001), and disease progression (P < 0.001). For monitoring treatment response and tumor burden, decreased methylation levels were observed in patients responding well to treatment, whereas increased levels were noted in cases of cancer progression or prior to metastasis.

Overall, detecting methylated GCM2 and TMEM240 in plasma offers a novel, accurate, and noninvasive method for monitoring breast cancer progression.

Poor dental health has been linked to an increased risk of gastric cancer (GC), but previous studies were limited by their retrospective design and relatively small sample size.

We followed a nationwide cohort of 5,888,034 Swedish adults over the age of 19 who visited a dentist between 2009 and 2016. Additionally, a nested case-control study was conducted by comparing incident GC cases to their siblings. Cox regression analyses, using attained age as the timescale and adjusting for potential confounders, were performed to evaluate the association between various dental health conditions and the risk of GC. In addition, we stratified our analyses by sex and age and conducted various sensitivity analyses to ensure the robustness of our findings.

Over an average follow-up of 6.4 years, we identified 3993 new GC cases, including 1241 cardia GC and 2752 non-cardia GC. Compared to individuals with healthy teeth, those with periodontitis had an 11% and 25% increased risk of GC and cardia GC, respectively. The positive associations between odontogenic inflammation and the risk of GC were consistent in sibling-controlled analyses. We also observed a dose-response relationship between the number of remaining teeth and the risk of GC, with fewer teeth associated with higher risks. Additionally, we did not find significant interactions between dental inflammatory conditions and the number of remaining teeth in relation to the risk of GC or its subtypes. Our findings were consistent across different sex and age subgroups and in sensitivity analyses.

This study provides the largest prospective cohort study evidence to date, along with the first sibling-controlled comparisons, supporting the association between poor dental health and GC risk. Promoting dental health in the general population could have significant public health implications in preventing this disease.

The Ki-67 protein is frequently employed in pathological immunohistochemistry to indicate cell proliferation activity. The principal aim of this study was to examine the impact of stratified management of Ki-67 on the clinicopathological characteristics and prognosis of patients with small-cell lung cancer (SCLC).

A total of 175 patients with SCLC who underwent surgical treatment were included in the study, with available data on the results of postoperative immunohistochemistry of the Ki-67 protein. A retrospective analysis was conducted to investigate the correlation between the protein and various clinicopathological features of SCLC, as well as its impact on survival.

The cut-off value for the Ki-67 level was determined to be 75% through receiver operating characteristic (ROC) analysis. An elevated Ki-67 level was found to be associated with preoperative chemotherapy (χ2 = 4.980, P = 0.028), preoperative radiotherapy (χ2 = 4.600, P = 0.032), T stage (χ2 = 4.173, P = 0.041), TNM staging (χ2 = 10.472, P = 0.005), and lymph node involvement (χ2 = 16.721, P < 0.0001). The results of the survival analysis indicated that patients with SCLC exhibiting high levels of Ki-67 had a poorer prognosis than those with low Ki-67 levels (P = 0.0004). This was particularly evident in patients aged 60 years or older (P = 0.034), in males (P = 0.046), smoking for a minimum of 30 years (P < 0.001), advanced T staging (T3 + T4) (P = 0.031), lymph node involvement (P = 0.038), and TNM staging (P = 0.015), were associated with poorer outcomes. The univariate Cox regression analysis indicated that exposure to tobacco consumption (P = 0.040), pathologic T stage (P = 0.047), lymph node metastasis (P = 0.002), TNM staging [Stage I vs. II (P = 0.016), Stage I vs. III (P = 0.003)], and Ki-67 positive rate (P < 0.001) were the factors related to prognosis in SCLC. The results of the multivariate regression analysis indicated that T stage(HR: 1.519, 95% CI: 1.116-2.015, P = 0.022), TNM staging[Stage I vs. III (HR: 2.310, 95% CI: 1.320-4.040, P < 0.001)], and Ki-67 expression(HR: 1.405, 95% CI: 1.025-1.810, P < 0.001) was identified as an additional risk factor for SCLC-related mortality.

In summary, the Ki-67 protein is not only strongly associated with the malignant characteristics of SCLC, but also the stratification of Ki-67 has significant implications for the treatment and prognosis of patients with small-cell lung cancer.

Not applicable.

Comorbid mental health symptoms impact 30-40% of cancer patients, significantly compromising treatment adherence and increasing mortality rates. Among patients undergoing radiation therapy, which is delivered with palliative intent in nearly half of all cases and for those nearing end-of-life, these rates may be even higher. Emerging research underscores the promising potential of psychedelic-assisted therapy (PAT) in alleviating cancer-related psychological distress. However, the perspectives of cancer patients on the therapeutic utility of psychedelics remain unexplored.

Adult patients with a cancer diagnosis were recruited in Radiation Oncology Clinic between May 2023 and August 2024. They included patients being evaluated before, during, or after radiation therapy. Data on demographics, medical history, prior psychedelic use, and measures of mental health burden and quality of life using validated questionnaires were collected to assess interest in PAT and factors associated with such interest.

100 patients enrolled in the study. 43% expressed interest in PAT, while 31% were opposed, and 26% were unsure. Prior diagnoses of mental health disorders like anxiety and depression, prior recreational psychedelic use, younger age, and male sex were positively associated with interest in PAT. Notably, patients with higher levels of depression, worse spiritual well-being, worse demoralization, worse quality of life, and more pain, symptoms that are targeted with PAT, were more likely to be receptive to it. Hesitancy was primarily attributed to a lack of information, cited by 43% of those not interested or unsure.

Psychedelic-assisted therapy represents a promising avenue to address critical gaps in cancer-related mental health care, and this study suggests that a substantial portion of cancer patients are receptive to and curious about this approach. The primary barrier to acceptance is informational, emphasizing the need for further research and education to dispel misconceptions and increase awareness of the safety and efficacy of psychedelic therapies. Future work should explore provider perspectives, patient outcomes, and the integration of PAT into palliative care frameworks.

Neurofibromatosis type 1 (NF1) is a genetic disorder associated with an increased risk of various cancers, including breast cancer. This report presents a case of a patient with NF1 who had a huge plexiform neurofibroma of the right breast and developed invasive carcinoma in the left breast.

A woman with a known history of NF1, enlarged right breast, and pectus carinatum presented with locally advanced breast cancer of the left breast. The patient underwent four cycles of neoadjuvant chemotherapy with doxorubicin and cyclophosphamide, followed by a left modified radical mastectomy and axillary lymph node dissection. Postoperative pathology showed a complete pathological response. Subsequently, the patient received four cycles of adjuvant paclitaxel followed by endocrine therapy with anastrozole which was replaced by tamoxifen due to bone and muscle pain. At one year follow-up, the patient remains free of disease. The patient was referred to a plastic surgeon for resection of the enlarged right breast.

It is known that women with NF1 have a higher risk of developing breast cancer, as demonstrated in our case. These patients could benefit from an early start of breast cancer screening, which could lead to an early diagnosis of early-stage tumors and a better prognosis. Additionally, enhanced access to healthcare centers and intensive surveillance could contribute significantly to better outcomes.

The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. Correlations between BCA data and demographic/clinical parameters were investigated. BCA-endotypes were identified and survival rates were compared between BCA-derived patient clusters. Patients with high-risk cytogenetics exhibited elevated cardiac marker index values. Across Revised-International Staging System (R-ISS) categories, BCA parameters did not show significant differences. However, both subcutaneous and total adipose tissue volumes were significantly lower in patients with progressive disease or death during follow-up compared to patients without progression. Cluster analysis revealed two distinct BCA-endotypes, with one group displaying significantly better survival. Furthermore, a combined model composed of clinical parameters and BCA data demonstrated a higher predictive capability for disease progression compared to models based solely on high-risk cytogenetics or R-ISS. These findings underscore the potential of BCA to improve patient stratification and refining prognostic models in MM.

In this study, we compared the performance of gadobutrol and meglumine gadoterate, two macrocyclic non-ionic and ionic contrast agents, for evaluating quantitative perfusion parameters of pituitary macroadenomas using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Patients with pituitary macroadenomas were prospectively enrolled and randomly assigned to be administered gadobutrol or meglumine gadoterate for MRI. Perfusion parameters of the pituitary macroadenoma, including Ktrans, Kep, Ve, and Vp, were measured using DCE-MRI. In total, 60 patients (mean age: 59.7 ± 13.7 years; 40 men) were evaluated. The non-inferiority test confirmed that gadobutrol was comparable to meglumine gadoterate for measuring the Ktrans of the pituitary macroadenoma. Kep was significantly higher with gadobutrol (P = 0.001) irrespective of tumor functional status and aggressiveness. Ktrans, Ve, and Vp and pre- and post-contrast T1-signal intensities of the tumor did not differ significantly for contrast agents. Perfusion parameters were not significantly associated with diagnostic performance in distinguishing the tumor functional status (P > 0.05). In summary, gadobutrol is non-inferior to meglumine gadoterate for the Ktrans measurement of pituitary macroadenomas. However, gadobutrol may lead to higher Kep, regardless of tumor functional status and aggressiveness. Awareness of this variation is crucial to preventing misinterpretation of vascular dynamics in pituitary macroadenomas.

Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-weighted magnetic resonance imaging (T2W-MRI) for automated tumor segmentation and differentiation between SIP and SIP-SCC.

We conducted a retrospective analysis of 174 patients diagnosed with SIPs, who were divided into a training cohort (n = 121) and a testing cohort (n = 53). Three DL architectures were utilized to train automated segmentation models for endoscopic and T2W-MRI images. DL scores predicting SIP-SCC were generated using DenseNet121 from both modalities and combined to create a dual-modality DL nomogram. The diagnostic performance of the DL models was assessed alongside two radiologists, evaluated through the area under the receiver operating characteristic curve (AUROC), with comparisons made using the Delong method.

In the testing cohort, the FCN_ResNet101 and VNet exhibited superior performance in automated segmentation, achieving mean dice similarity coefficients of 0.95 ± 0.03 for endoscopy and 0.93 ± 0.02 for T2W-MRI, respectively. The dual-modality DL nomogram based on automated segmentation demonstrated the highest predictive performance for SIP-SCC (AUROC 0.865), outperforming the radiology resident (AUROC 0.672, p = 0.071) and the attending radiologist (AUROC 0.707, p = 0.066), with a trend toward significance. Notably, both radiologists improved their diagnostic performance with the assistance of the DL nomogram (AUROCs 0.734 and 0.834).

The DL framework integrating endoscopy and T2W-MRI offers a fully automated predictive tool for SIP-SCC.

The integration of endoscopy and T2W-MRI within a well-established DL framework enables fully automated prediction of SIP-SSC, potentially improving decision-making for patients with suspicious SIP.

Detecting the transformation of SIP into SIP-SCC before surgery is both critical and challenging. Endoscopy and T2W-MRI were integrated using DL for predicting SIP-SCC. The dual-modality DL nomogram outperformed two radiologists. The nomogram may improve decision-making for patients with suspicious SIP.

The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) among esophageal cancer resection patients. Clinical data of 396 patients with esophageal cancer who underwent esophagectomy from November 2020 to June 2023 in the electronic medical records of cardiothoracic Surgery, Affiliated Hospital of Jiangnan University. Participants were randomly divided into training and testing sets in a 7:3 ratio. Predictors were screened by Least absolute shrinkage and selection operator (LASSO) regression analysis and a nomogram model was built. The discrimination and consistency of the model were evaluated using the area under the receiver operating characteristic curve (AUC), C-statistic, Brier score, Hosmer-Lemeshow goodness-of-fit test, calibration curve and decision curve analysis (DCA). The results were validated using 1000 bootstraps resampling internal validation and testing set. Among 32 potential predictors, the final prediction model included 6 variables: postoperative pain, postoperative infection, dexmedetomidine use, propofol use, duration of mechanical ventilation, and Prognostic Nutritional Index (PNI). The model showed a good discrimination with an AUC of 0.919 (95% CI: 0.885- 0.953) in the training set, and adjusted to 0.911 (95% CI: 0.878- 0.944) and 0.871 (95% CI: 0.802- 0.940) in the internal validation and the testing set, respectively. ROC curves, calibration curves, DCA curves, C-statistic, Brier score and Hosmer-Lemeshow goodness-of-fit test showed excellent model performance. This study successfully established and validated the first POD prediction model for patients with esophageal cancer resection. It could accurately predict the occurrence of POD and effectively identify the high-risk patients, which is of great significance for improving the risk stratification of the population and for implementing targeted prevention intervention measures.