Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using MR imaging

BACKGROUND: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation

Measurement of Hydrogen Diffusion through PVA Hydrogel using Magnetic Resonance Imaging Method in Comparison with Consistency Assessment using Digital Penetrometer

A study of hydrogen diffusion behavior in the PVA (polyvinyl-alcohol) hydrogel had been done to measure diffusion coefficient (Apparent Diffusion Coefficient/ ADC) used as parameter of brain tumor grading. For our study, PVA hydrogel samples were prepared using freezing-thawing method with various concentration and number of freezing-thawing cycles. The ambiguity of brain tumor grading using ADC value, which is formed from Diffusion Weighted-Imaging 1,5 T, could be reduced enough with observing the result of correlation the ADC at b-value 1000 s/mm2 and 3000 s/mm2 with assessment of consistency measured using digital penetrometer based on microcontroller

Is there a correlation between 18F-FDG-PET standardized uptake value, T-classification, histological grading and the anatomic subsites in newly diagnosed squamous cell carcinoma of the head and neck?

18F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET)/CT imaging of squamous cell carcinoma of the head and neck (HNSCC) renders the possibility to study metabolic tumor activity by measuring FDG-uptake expressed as maximum standardized uptake value (SUVmax). A correlation between SUVmax and several factors including T-classification, histological tumor differentiation or different anatomic subsites is of potential interest in HNSCC. The aim of this study was to evaluate how metabolic tumor activity derived from FDG-PET correlates with prognostic clinical and pathological parameters including these factors. 262 patients with HNSCC undergoing PET/CT for initial staging were assessed separately for a potential correlation between SUVmax and T-classification, histological grading, and anatomical subsites of the primary tumor. Nonparametric testing showed a significant correlation between SUVmax and T-classification (P<0.001). On the contrary, no statistically significant correlation was found between SUVmax and histological tumor grading. Furthermore, no statistical significant correlation between the different anatomical subsites and SUVmax were found. There was no significant correlation of SUVmax and tumor grading after adjustment for T-stage and anatomical localization of the tumor, neither. Conclusion: Metabolic tumor activity correlates with T-stage of HNSCC. However, histological tumor grading does not correlate with SUVmax. The role of primary tumor SUVmax as a predictor of outcome or survival remains unclear. Clinicians should therefore exercise caution in attributing any clinical importance to SUVmax obtained from a single PET/CT exa

Prognostic significance of primary-tumor extension, stage and grade of nuclear differentiation in patients with renal cell carcinoma

Surgery remains the preferred therapy for renal cell carcinoma. The various adjunctive or complementary therapies currently yield disappointing results. Identifying reliable prognostic factors could help in selecting patients most likely to benefit from postoperative adjuvant therapies. We reviewed the surgical records of 78 patients who had undergone radical nephrectomy with lymphadenectomy for renal cell carcinoma, matched for type of operation and histology. According to staging (TNM), 5.1% of the patients were classified as stage I, 51.3% as stage II, 29.5% as stage III and 14.5% as stage IV. Of the 78 patients 40 were T2N0 and 21 T3aN0. Tumor grading showed that 39.7% of the patients had well-differentiated tumors(G1), 41.1% moderately-differentiated (G2), and 19.2% poorly-differentiated tumors (G3). Overall actuarial survival at 5 and 10 years was 100% for stage 1; 91.3% at 5 years and 83.1% at 10 years for stage II; 45.5% and 34.1% for stage III; and 29.1% and nil for stage IV (stage II vs stage III p = 0.0001). Patients with tumors confined to the kidney (pT2N0) had better 5- and 10-year survival rates than patients with tumors infiltrating the perirenal fat (pT3aN0) (p = 0.000006). Survival differed according to nuclear grading (G1 vs G3 ; p = 0.000005; G2 vs G3; p = 0.0009). In conclusion our review identified tumor stage, primary-tumor extension, and the grade of nuclear differentiation as reliable prognostic factors in patients with renal cell carcinomas

High survivin expression as a risk factor in patients with anal carcinoma treated with concurrent chemoradiotherapy

Purpose: To investigate the prognostic value of survivin expression in pretreatment specimens from patients with anal cancer treated with concurrent 5-FU and mitomycin C-based chemoradiation (CRT). Material and methods: Immunohistochemical staining for survivin was performed in pretreatment biopsies of 62 patients with anal carcinoma. Survivin expression was correlated with clinical and histopathological characteristics as well as local failure free- (LFFS), distant metastases free- (DMFS), cancer specific- (CSS), and overall survival (OS). Results: Survivin staining intensity was weak in 10%, intermediate in 48% and intense in 42% of the patients. No association between survivin expression and clinicopathologic factors (tumor stage, age and HIV status) could be shown. In univariate analysis, the level of survivin staining was significantly correlated with DMFS (low survivin vs. high survivin: 94% vs. 74%, p=0.04). T-stage, N-stage and the tumor grading were significantly associated with OS and CSS and with DMFS and LFFS, respectively. In multivariate analysis, survivin was confirmed as independent prognostic parameter for DMFS (RR, 0.04; p=0.02) and for OS (RR, 0.27; p=0.04). Conclusion: Our results demonstrated that the level of pretreatment survivin is correlated with the clinical outcome in patients with anal carcinoma treated with concurrent CRT. Further studies are warranted to elucidate the complex role of survivin for the oncologic treatment and to exploit the protein as a therapeutic target in combined modality treatment of anal cancer

Augmented Mitotic Cell Count using Field Of Interest Proposal

Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.Comment: 6 pages, submitted to BVM 2019 (bvm-workshop.org

The role of different adjuvant therapies in locally advanced gastric adenocarcinoma

Complete surgical resection remains the only curative treatment option in locally advanced gastric cancer (GC). Several studies were conducted to prevent local recurrence and to increase the chance of cure. The aim of this study was to summarize our experience in locally advanced GC patients treated with adjuvant chemoradiotherapy (CRT) and to evaluate overall survival (OS), disease-free survival (DFS), toxicity rate and compliance to treatment