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Purpose Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient’s prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. Methods HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. Results Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. Conclusion The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient’s prognosis.

ObjectivesTexture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.MethodsA total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.ResultsThe ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.ConclusionTexture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.Key Points• Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment.• Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma.• Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.

Background The value of chemotherapy in soft tissue sarcoma (STS) remains controversial. Several expert teams consider that chemotherapy provides a survival advantage and should be proposed in high-risk (HR) patients. However, the lack of accuracy in identifying HR patients with conventional risk factors (large, deep, FNCLCC grade 3, extremity STS) is an issue that cannot be neglected. For example, while the FNCLCC grading system is a powerful tool, it has several limitations. CINSARC, a 67-gene signature, has proved to be an additional independent factor for predicting metastatic spread and outperforms histological grade. Regardless of FNCLCC grade, CINSARC stratifies patients into two separate prognostic groups: one with an excellent prognosis (low-risk (LR) CINSARC) and the other with a worse outcome (HR-CINSARC) in terms of metastatic relapse. Here we evaluate the role of chemotherapy in grade 1–2 STS patients with HR-CINSARC and assess the prognostic value of CINSARC in patients treated with standard of care. Methods CHIC is a parallel, randomized, open-label, multicenter study evaluating the effect on metastasis-free survival of adding perioperative chemotherapy to standard of care in patients with grade ½ STS sarcoma defined as HR by CINSARC. In this target selection design, 600 patients will be screened with CINSARC to randomize 250 HR-CINSARC patients between standard of care and standard of care plus chemotherapy (4 cycles of 3 weeks of intravenous chemotherapy with doxorubicin in combination with dacarbazine or ifosfamide according to histologic subtype). LR-CINSARC patients will be treated by standard of care according to the investigator. The primary endpoint is metastasis-free survival. Secondary endpoints include overall survival, disease-free survival and safety. Furthermore, the prognostic value of CINSARC will be evaluated by comparing LR-CINSARC patients to HR-CINSARC patients randomized in standard of care. Discussion CHIC is a prospective randomized phase III trial designed to comprehensively evaluate the benefit of chemotherapy in HR-CINSARC patients and to prospectively validate the prognostic value of CINSARC in grade ½ STS sarcoma patients. Trial registration ClinicalTrials.gov identifier: NCT04307277 Date of registration: 13 March 2020

A small number of nomograms have been previously developed to predict the individual survival of patients who undergo curative resection for gastric cancer. However, all were derived from single high-volume centers. The aim of this study was to develop and validate a nomogram for gastric cancer patients using a multicenter database. We reviewed the clinicopathological and survival data of 2012 patients who underwent curative resection for gastric cancer between 2001 and 2006 at eight centers. Among these centers, six institutions were randomly assigned to the development set, and the other two centers were assigned to the validation set. Multivariate analysis using the Cox proportional hazard regression model was performed, and discrimination and calibration were evaluated by external validation. Multivariate analyses revealed that age, tumor size, lymphovascular invasion, depth of invasion, and metastatic lymph nodes were significant prognostic factors for overall survival. In the external validation, the concordance index was 0.831 (95% confidence interval, 0.784-0.878), and Hosmer-Lemeshow chi-square statistic was 3.92 (P = 0.917). We developed and validated a nomogram to predict 5-year overall survival after curative resection for gastric cancer based on a multicenter database. This nomogram can be broadly applied even in general hospitals and is useful for counseling patients, and scheduling follow-up.

Background: We aimed to assess the prognostic relevance of the new Grade Groups in Prostate Cancer (PCa) within a large cohort of European men treated with radical prostatectomy (RP). Methods: Data from 27 122 patients treated with RP at seven European centers were analyzed. We investigated the prognostic performance of the new Grade Groups (based on Gleason score 3+3, 3+4, 4+3, 8 and 9–10) on biopsy and RP specimen, adjusted for established clinical and pathological characteristics. Multivariable Cox proportional hazards regression models assessed the association of new Grade Groups with biochemical recurrence (BCR). Prognostic accuracies of the models were assessed using Harrell’s C-index. Results: Median follow-up was 29 months (interquartile range, 13–54). The 4-year estimated BCR-free survival (bRFS) for biopsy Grade Groups 1–5 were 91.3, 81.6, 69.8, 60.3 and 44.4%, respectively. The 4-year estimated bRFS for RP Grade Groups 1–5 were 96.1%, 86.7%, 67.0%, 63.1% and 41.0%, respectively. Compared with Grade Group 1, all other Grade Groups based both on biopsy and RP specimen were independently associated with a lower bRFS (all P <0.01). Adjusted pairwise comparisons revealed statistically differences between all Grade Groups, except for group 3 and 4 on RP specimen ( P =0.10). The discriminations of the multivariable base prognostic models based on the current three-tier and the new five-tier systems were not clinically different (0.3 and 0.9% increase in discrimination for clinical and pathological model). Conclusions: We validated the independent prognostic value of the new Grade Groups on biopsy and RP specimen from European PCa men. However, it does not improve the accuracies of prognostic models by a clinically significant margin. Nevertheless, this new classification may help physicians and patients estimate disease aggressiveness with a user-friendly, clinically relevant and reproducible method.

In a blind, dual-center, multi-observer setting, we here identify the pre-treatment radiologic features by Magnetic Resonance Imaging (MRI) associated with subsequent treatment options in patients with glioma. Study included 220 previously untreated adult patients from two institutions (94 + 126 patients) with a histopathologically confirmed diagnosis of glioma after surgery. Using a blind, cross-institutional and randomized setup, four expert neuroradiologists recorded radiologic features, suggested glioma grade and corresponding confidence. The radiologic features were scored using the Visually AcceSAble Rembrandt Images (VASARI) standard. Results were retrospectively compared to patient treatment outcomes. Our findings show that patients receiving a biopsy or a subtotal resection were more likely to have a tumor with pathological MRI-signal (by T2-weighted Fluid-Attenuated Inversion Recovery) crossing the midline (Hazard Ratio; HR = 1.30 [1.21–1.87], P  < 0.001), and those receiving a biopsy sampling more often had multifocal lesions (HR = 1.30 [1.16–1.64], P  < 0.001). For low-grade gliomas (N = 50), low observer confidence in the radiographic readings was associated with less chance of a total resection ( P  = 0.002) and correlated with the use of a more comprehensive adjuvant treatment protocol (Spearman = 0.48, P  < 0.001). This study may serve as a guide to the treating physician by identifying the key radiologic determinants most likely to influence the treatment decision-making process.

Purpose Prostate cancer is the second most common cancer in men and the sixth most common cause of death from cancer in men worldwide. Currently, a sufficient pathological distinction between patients requiring further treatment and those for which active surveillance remains an option is still lacking, which leads to the problem of overtreatment. Cell proliferation is routinely assessed by detecting Ki-67 antigen. While Ki-67 is expressed throughout the interphase of proliferating cells, phosphorylation of the chromatin constituent histone H3 occurs only during the late G2 phase and mitosis thus providing a more strict assessment of the mitotic activity. We undertook this study to test whether expression of the recently introduced proliferation marker phospho-histone H3 (pHH3) in prostate carcinoma tissue sections exhibits prognostic significance in comparison with Ki-67. Methods Protein expression of pHH3 and Ki-67 was assessed on TMA consisting of paraffin-embedded tissue from men that had undergone radical prostatectomy. The analysis included triplicate tissue cores of a total of 339 tumor foci. Immunohistochemical staining of pHH3 and Ki-67 was performed and analyzed using Definiens imaging software. Results Prostate cancer tissue exhibited a significantly higher frequency of pHH3-positive cells compared to benign prostate tissue. pHH3 expression was significantly correlated with Ki-67 expression. Furthermore, statistical analysis revealed positive correlation between pHH3 expression and PSA levels at diagnosis and in addition negatively correlated with overall survival. In contrast to Ki-67 staining, pHH3 expression did not correlate with Gleason grade. Conclusion Our data point to a conceivable role of pHH3 as prognostic biomarker in prostate carcinoma.

Background Histologic grade is the most important predictor of the clinical outcome of non-muscle invasive (Ta, T1) papillary urothelial carcinoma (NMIPUCa), but its ambiguous criteria diminish its power to predict recurrence/progression for individual patients. We attempted to find an objective and reproducible histologic predictor of NMIPUCa that correlates well with the clinical outcome. Methods A total of 296 PUCas were collected from the Departments of Surgical Pathology of 11 institutions in South Korea. The clinical outcome was grouped into no event (NE), recurrence (R), and progression (P) categories. All 25 histological parameters were numerically redefined. The clinical pathology of each case was reviewed individually by 11 pathologists from 11 institutions based on the 2004 WHO criteria and afterwards blindly evaluated by two participants, based on our proposed parameters. Univariate and multivariate logistic regression analyses were performed using the R software package. Results The level of mitoses was the most reliable parameter for predicting the clinical outcome. We propose a four-tiered grading system based on mitotic count (> 10/10 high-power fields), nuclear pleomorphism (smallest-to-largest ratio of tumor nuclei >20), presence of divergent histology, and capillary proliferation (> 20 capillary lumina per papillary core). Conclusions The level of mitoses at the initial bladder biopsy and transurethral resection (TUR) specimen appeared to be an independent predictor of the Ta PUCa outcome. Other parameters include the number of mitoses, nuclear pleomorphism, divergent histology, and capillary proliferation within the fibrovascular core. These findings may improve selection of patients for a therapeutic strategy as compared to previous grading systems.

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Aneuploidy, defined as chromosome gains and losses, is a hallmark of cancer. However, compared with other tumor types, extensive aneuploidy is relatively rare in prostate cancer. Thus, whether numerical chromosome aberrations dictate disease progression in prostate cancer patients is not known. Here, we report the development of a method based on whole-transcriptome profiling that allowed us to identify chromosome-arm gains and losses in 333 primary prostate tumors. In two independent cohorts (n = 404) followed prospectively for metastases and prostate cancer-specific death for a median of 15 years, increasing extent of tumor aneuploidy as predicted from the tumor transcriptome was strongly associated with higher risk of lethal disease. The 23% of patients whose tumors had five or more predicted chromosome-arm alterations had 5.3 times higher odds of lethal cancer (95% confidence interval, 2.2 to 13.1) than those with the same Gleason score and no predicted aneuploidy. Aneuploidy was associated with lethality even among men with high-risk Gleason score 8-to-10 tumors. These results point to a key role of aneuploidy in driving aggressive disease in primary prostate cancer.