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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.

Since 1966, when Donald Gleason, MD, first proposed grading prostate cancer based on its histologic architecture, there have been numerous changes in clinical and pathologic practices relating to prostate cancer. Patterns 1 and 2, comprising more than 30% of cases in the original publications by Gleason, are no longer reported on biopsy and are rarely diagnosed on radical prostatectomy. Many of these cases may even have been mimickers of prostate cancer that were described later with the use of contemporary immunohistochemistry. The original Gleason system predated many newly described variants of prostate cancer and our current concept of intraductal carcinoma. Gleason also did not describe how to report prostate cancer on biopsy with multiple cores of cancer or on radical prostatectomy with separate tumor nodules. To address these issues, the International Society of Urological Pathology first made revisions to the grading system in 2005, and subsequently in 2014. Additionally, a new grading system composed of Grade Groups 1 to 5 that was first developed in 2013 at the Johns Hopkins Hospital and subsequently validated in a large multi-institutional and multimodal study was presented at the 2014 International Society of Urological Pathology meeting and accepted both by participating pathologists as well as urologists, oncologists, and radiation therapists. In the present study, we describe updates to the grading of prostate cancer along with the new grading system.

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.

A recently described nuclear grading system predicted survival in patients with epithelioid malignant pleural mesothelioma. The current study was undertaken to validate the grading system and to identify additional prognostic factors. We analyzed cases of epithelioid malignant pleural mesothelioma from 17 institutions across the globe from 1998 to 2014. Nuclear grade was computed combining nuclear atypia and mitotic count into a grade of I–III using the published system. Nuclear grade was assessed by one pathologist for three institutions, the remaining were scored independently. The presence or absence of necrosis and predominant growth pattern were also evaluated. Two additional scoring systems were evaluated, one combining nuclear grade and necrosis and the other mitotic count and necrosis. Median overall survival was the primary endpoint. A total of 776 cases were identified including 301 (39%) nuclear grade I tumors, 354 (45%) grade II tumors and 121 (16%) grade III tumors. The overall survival was 16 months, and correlated independently with age ( P =0.006), sex (0.015), necrosis (0.030), mitotic count (0.001), nuclear atypia (0.009), nuclear grade (<0.0001), and mitosis and necrosis score (<0.0001). The addition of necrosis to nuclear grade further stratified overall survival, allowing classification of epithelioid malignant pleural mesothelioma into four distinct prognostic groups: nuclear grade I tumors without necrosis (29 months), nuclear grade I tumors with necrosis and grade II tumors without necrosis (16 months), nuclear grade II tumors with necrosis (10 months) and nuclear grade III tumors (8 months). The mitosis–necrosis score stratified patients by survival, but not as well as the combination of necrosis and nuclear grade. This study confirms that nuclear grade predicts survival in epithelioid malignant pleural mesothelioma, identifies necrosis as factor that further stratifies overall survival, and validates the grading system across multiple institutions and among both biopsy and resection specimens. An alternative scoring system, the mitosis–necrosis score is also proposed.

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and 0.50 (range 0.40–0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

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.

Mucoepidermoid carcinoma (MEC) is the most common carcinoma of the salivary glands. Here, we have used two large patient cohorts with MECs comprising 551 tumors to study clinical, histological, and molecular predictors of survival. One cohort (n = 167), with known CRCT1/3-MAML2 fusion status, was derived from the Hamburg Reference Centre (HRC; graded with the AFIP and Brandwein systems) and the other (n = 384) was derived from the population-based Cancer Registry of North Rhine-Westphalia (LKR-NRW; graded with the AFIP system). The reliability of both the AFIP and Brandwein grading systems was excellent (n = 155). The weighted kappa for inter-rater agreement was 0.81 (95% CI 0.65–0.97) and 0.83 (95% CI 0.71–0.96) for the AFIP and Brandwein systems, respectively. The 5-year relative survival was 79.7% (95% CI 73.2–86.2%). Although the Brandwein system resulted in a higher rate of G3-MECs, survival in G3-tumors (AFIP or Brandwein grading) was markedly worse than in G1/G2-tumors. Survival in > T2 tumors was markedly worse than in those with lower T-stage. Also, fusion-negative MECs had a worse 5-year progression-free survival. The frequency of fusion-positive MECs in the HRC cohort was 78.4%, of which the majority (86.7%) was G1/G2-tumors. In conclusion, the AFIP and Brandwein systems are useful in estimating prognosis and to guide therapy for G3-MECs. However, their significance regarding young age (≤ 30 years) and location-dependent heterogeneity of in particular G2-tumors is more questionable. We conclude that CRTC1/3-MAML2 testing is a useful adjunct to histologic scoring of MECs and for pinpointing tumors with poor prognosis with higher precision, thus avoiding overtreatment.

Three pathological grading systems advocated by Perzin/Szanto, Spiro, and van Weert are currently used for adenoid cystic carcinoma (AdCC). In these systems, the amount or presence of the solid tumor component in AdCC specimens is an important index. However, the “solid tumor component” has not been well defined. Salivary AdCC cases (N = 195) were collected after a central pathology review. We introduced a novel criterion for solid tumor component, minAmax (minor axis maximum). The largest solid tumor nest in each AdCC case was histologically screened, the maximum oval fitting the solid nest was estimated, and the length of the minor axis of the oval (minAmax) was measured. The prognostic cutoff for the minAmax was determined using training and validation cohorts. All cases were evaluated for the four grading systems, and their prognostic impact and interobserver variability were examined. The cutoff value for the minAmax was set at 0.20 mm. Multivariate prognostic analyses showed the minAmax and van Weert systems to be independent prognostic tools for overall, disease‐free, and distant metastasis‐free survival while the Perzin/Szanto and Spiro systems were selected for overall survival but not for disease‐free or distant metastasis‐free survival. The highest hazard ratio for overall survival (11.9) was obtained with the minAmax system. The reproducibility of the minAmax system (kappa coefficient of 0.81) was scored as very good while those of the other three systems were scored as moderate. In conclusion, the minAmax is a simple, objective, and highly reproducible grading system useful for prognostic stratification for salivary AdCC. The amount or presence of the solid tumor component is an important index for histopathological grading of adenoid cystic carcinoma. However, the “solid tumor component” has not been well defined. We introduced a novel objective criterion for solid tumor component, minAmax (minor axis maximum), and showed that the minAmax is a simple, objective, and highly reproducible grading system useful for prognostic stratification for salivary adenoid cystic carcinoma.

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 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