Automatic digital quantification of bone marrow myeloma volume in appendicular skeletons - clinical implications and prognostic significance

Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma

Multiple myeloma (MM) is a hematopoietic malignancy characterized by heterogeneity, which corresponds to alternative splicing (AS) profiles and disadjust gene expression. Bioinformatics analysis of AS factors possibly related to MM progression identified the polypyrimidine tract binding protein (PTBP1) as candidate. The purpose of this study was to confirm the incidence and prognostic value of PTBP1 in MM patients. Several cohorts of 2971 patients presenting newly diagnosed and relapsed MM were enrolled. Correlations between PTBP1 expression and clinicopathological characteristics, proliferative activity, and response to therapy of myeloma cells were analyzed. Moreover, the effect of PTBP1 on the AS pattern of specific aerobic glycolysis-related genes was explored in MM patients. Clinically, PTBP1 expression was present at all stages; it increased with disease progression and poor prognosis, which was even stronger elevated in patients with high tumor burden and drug resistance. Mechanistically, PTBP1 modulated AS of PKM2 and aerobic glycolysis-related genes in MM patients, which play synergistic or additive effects in clinical outcome. PTBP1 may be a novel marker for prognostic prediction and a promising therapeutic target for the development of anti-MM treatments.

Promising Prognosis Marker Candidates on the Status of Epithelial-Mesenchymal Transition and Glioma Stem Cells in Glioblastoma

Multivariable analyses of global expression profiling are valid indicators of the prognosis of various diseases including brain cancers. To identify the candidates for markers of prognosis of glioblastoma, we performed multivariable analyses on the status of epithelial (EPI)-mesenchymal (MES) transition (EMT), glioma (GLI) stem cells (GSCs), molecular target therapy (MTT), and potential glioma biomarkers (PGBs) using the expression data and clinical information from patients. Random forest survival and Cox proportional hazards regression analyses indicated significant variable values for DSG3, CLDN1, CDH11, FN1, HDAC3/7, PTEN, L1CAM, OLIG2, TIMP4, IGFBP2, and GFAP. The analyses also comprised prognosis prediction formulae that could distinguish between the survival curves of the glioblastoma patients. In addition to the genes mentioned above, HDAC1, FLT1, EGFR, MGMT, PGF, STAT3, SIRT1, and GADD45A constituted complex genetic interaction networks. The calculated status scores obtained by principal component analysis indicated that GLI genes covered the status of EPI, GSC, and MTT-related genes. Moreover, survival tree analyses indicated that MES^high, MES^highGLI^low, GSC^highGLI^low, MES^highMTT^low, and PGB^high showed poor prognoses and MES^middle, GSC^low, and PGB^low showed good prognoses, suggesting that enhanced EMT and GSC are associated with poor survival and that lower expression of EPI markers and the pre-stages of EMT are relatively less malignant in glioblastoma. These results demonstrate that the assessment of EMT and GSC enables the prediction of the prognosis of glioblastoma that would help develop novel therapeutics and de novo marker candidates for the prognoses of glioblastoma.

Assessment of tumor volume and density as a measure of the response of advanced hepatocellular carcinoma to sorafenib: Application of automated measurements on computed tomography scans

Background and Aim

To better predict patient survival, we used automated tumor volume and density measurements to make an objective radiological assessment of the response of advanced hepatocellular carcinoma (HCC) to treatment with sorafenib.

Methods

Patients treated with sorafenib were identified retrospectively. Those who were diagnosed with Child‐Pugh class A liver function, Barcelona‐Clinic Liver Cancer stage C, and Eastern Cooperative Oncology Group performance status grade 0/1 were enrolled (n = 22). Reviews of contrast‐enhanced computed tomography images were supported by the automated measurement of lesions using computer software. Treatment responses were assessed using volume and density criteria. Kaplan–Meier methods and multivariate Cox regression analysis were used to evaluate treatment responses and identify the most significant prognostic factors for overall survival (OS).

Results

After patients were dichotomized according to volume and density criteria, the median OS for those with an objective response (OR) (complete response + partial response) was 20.4 months and that for those with a non‐OR (stable disease + progressive disease) was 9.3 months (P = 0.009). The best multivariate regression model for survival identified volume and density criteria (OR or non‐OR) as a significant variable, along with baseline alpha‐fetoprotein levels (log‐rank test, P = 0.01). No other conventional criteria were identified as significant.

Conclusions

Tumor volume and density assessment using automated lesion measurements may be an objective method of evaluating responses of advanced HCC to treatment with sorafenib.

Prognostic significance of bone marrow abnormalities in the appendicular skeleton of patients with multiple myeloma

We aimed to determine the clinical and prognostic significance of medullary abnormalities detected by low-dose whole-body multidetector computed tomography (MDCT) in the appendicular skeleton (AS) of patients with newly diagnosed symptomatic multiple myeloma (MM). One hundred ninety-six patients underwent low-dose whole-body MDCT as an initial workup. Patients were categorized into 3 groups based on the medullary pattern of the AS: fatty (36.3%), focal (43.4%), and diffuse (20.4%). Medullary abnormalities were associated with Durie-Salmon and revised International Scoring System stage 3, creatinine levels >2.0 mg/dL, and the proportion of bone marrow plasma cells. The median follow-up was 35.4 months. Patients with fatty, focal, and diffuse patterns had a median survival of not reached, 56 months, and 38 months, respectively. Overall survival (OS) was associated with age, Durie-Salmon stage 3, creatinine levels >2.0 mg/dL, ineligibility for autologous stem cell transplantation, and focal and diffuse patterns on univariate analysis. Multivariate analysis showed that age and diffuse pattern (hazard ratio [HR], 1.92; 95% confidence interval [CI], 1.12-3.31; P = .018) were independent predictors of progression-free survival. Age and focal (HR, 2.51; 95% CI, 1.14-5.56; P = .023) and diffuse (HR, 4.12; 95% CI, 1.74-9.77; P = .001) patterns were also independent predictors of OS. The addition of marrow pattern to preexisting risk factors was associated with a net reclassification improvement for predicting OS (to 0.37, P = .015). Medullary abnormalities in the AS (detected by low-dose whole-body MDCT) are associated with a poor prognosis, independent of other clinical variables.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.