The Significance of Accurate Determination of Gleason Score for Therapeutic Options and Prognosis of Prostate Cancer

Deep Learning Model for Predicting Lung Adenocarcinoma Recurrence from Whole Slide Images

Lung cancer is the leading cause of cancer-related death in the United States. Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer that can be treated with resection. While resection can be curative, there is a significant risk of recurrence, which necessitates close monitoring and additional treatment planning. Traditionally, microscopic evaluation of tumor grading in resected specimens is a standard pathologic practice that informs subsequent therapy and patient management. However, this approach is labor-intensive and subject to inter-observer variability. To address the challenge of accurately predicting recurrence, we propose a deep learning-based model to predict the 5-year recurrence of LUAD in patients following surgical resection. In our model, we introduce an innovative dual-attention architecture that significantly enhances computational efficiency. Our model demonstrates excellent performance in recurrent risk stratification, achieving a hazard ratio of 2.29 (95% CI: 1.69-3.09, p < 0.005), which outperforms several existing deep learning methods. This study contributes to ongoing efforts to use deep learning models for automatically learning histologic patterns from whole slide images (WSIs) and predicting LUAD recurrence risk, thereby improving the accuracy and efficiency of treatment decision making.

Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning

Abnormal DNA ploidy, found in numerous cancers, is increasingly being recognized as a contributor in driving chromosomal instability, genome evolution, and the heterogeneity that fuels cancer cell progression. Furthermore, it has been linked with poor prognosis of cancer patients. While next-generation sequencing can be used to approximate tumor ploidy, it has a high error rate for near-euploid states, a high cost and is time consuming, motivating alternative rapid quantification methods. We introduce PloiViT, a transformer-based model for tumor ploidy quantification that outperforms traditional machine learning models, enabling rapid and cost-effective quantification directly from pathology slides. We trained PloiViT on a dataset of fifteen cancer types from The Cancer Genome Atlas and validated its performance in multiple independent cohorts. Additionally, we explored the impact of self-supervised feature extraction on performance. PloiViT, using self-supervised features, achieved the lowest prediction error in multiple independent cohorts, exhibiting better generalization capabilities. Our findings demonstrate that PloiViT predicts higher ploidy values in aggressive cancer groups and patients with specific mutations, validating PloiViT potential as complementary for ploidy assessment to next-generation sequencing data. To further promote its use, we release our models as a user-friendly inference application and a Python package for easy adoption and use.

DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study

PURPOSE

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

METHODS

A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.

RESULTS

The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).

CONCLUSION

The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

Exploring the intratumoral heterogeneity of DNA ploidy in prostate cancer

BACKGROUND

Prostate cancer is morphologically and molecularly heterogeneous. Genomic heterogeneity might be mirrored by variability in DNA ploidy. Aneuploidy is a hallmark of genomic instability and associated with tumor aggressiveness. Little attention has been paid to the biological significance of the diploid tumor cell population that often coexists with aneuploid populations. Here, we investigated the role of DNA ploidy in tumor heterogeneity and clonal evolution.

METHODS

Three radical prostatectomy specimens with intratumoral heterogeneity based on nuclear features on H&E were selected. DNA content of each subpopulation was determined by DNA image cytometry and silver in situ hybridization (SISH). Genomic evolution was inferred from array comparative genomic hybridization (aCGH). Additionally, immunohistochemistry was used to examine the stemness-associated marker ALDH1A1.

RESULTS

Nuclear morphology reliably predicted DNA ploidy status in all three cases. In one case, aCGH analysis revealed several shared deletions and one amplification in both the diploid and the aneuploid population, suggesting that these populations could be related. In the other two cases, a statement about relatedness was not possible. Furthermore, ALDH1A1 was expressed in 2/3 cases and exclusively observed in their diploid populations.

CONCLUSIONS

In this proof-of-concept study, we demonstrate the feasibility to predict the DNA ploidy status of distinct populations within one tumor by H&E morphology. Future studies are needed to further investigate the clonal relationship between the diploid and the aneuploid subpopulation and test the hypothesis that the aneuploid population is derived from the diploid one. Finally, our analyses pointed to an enrichment of the stemness-associated marker ALDH1A1 in diploid populations, which warrants further investigation in future studies.

The prognostic impact of lung adenocarcinoma predominance classification relating to pathological factors in lobectomy, the Japanese Joint Committee of Lung Cancer Registry Database in 2010

Objective

We studied the prognosis and clinicopathological background of lung adenocarcinoma predominance among patients who underwent lobectomy using data from the Japanese Joint Committee of Lung Cancer Registry.

Methods

Two thousand eight hundred sixty-three cases were extracted. Recurrence free survival (RFS) rates, overall survival (OS) rates and clinicopathological factors and epidermal growth factor receptor (EGFR) mutation status were examined.

Results

Median follow-up period was 65.5 months. Adenocarcinoma predominance was sub-grouped according to OS and RFS rate. In pathological stage I, 5-year RFS and OS rates were respectively 92.2% and 95.8% in group A (adenocarcinoma-in-situ + minimally invasive adenocarcinoma), 89.3% and 92.1% in group B (lepidic), 79.2% and 89.7% in group C (papillary + acinar + variants) and 69.0% and 79.0% in group D (solid + micropapillary). In pathological stage II + IIIA, they were, 43.6% and 72.4% in B, 39.5% and 66.9% in C and 31.0% and 53.7% in D. Group D showed significant worst outcome both in stage I and II + IIIA. Up stage rate from clinical stage I to pathological stage II + IIIA was 0.0%, 3.7%, 15.9% and 33.3%. The frequency of lymph-vessel, vascular, pleura invasion and positive EGFR mutation were 0.0%, 0.0%, 0.0% and 57.1% in group A, 15.6%, 10.0%, 12.1% and 55.1% in B, 36.6%, 31.8%, 29.7% and 44.9% in C, 50.2%, 57.8%, 38.9% and 21.3% in D. In group D, lymph-vessel, vascular and pleura invasion were most, EGFR mutation was least frequent not only in pathological stage I but also stage II + IIIA. In multivariate analysis, age, pathological stage, vascular invasion, and group D were independent factors affected RFS and OS.

Conclusion

Limited to lobectomy cases, solid + micropapillary was independent prognostic factor both in early and locally advanced stage. Its malignant degree was related to the frequency of pathological invasive factors and EGFR mutation status.

Establishment of Prognostic Nomograms for Early-Onset Prostate Cancer Patients: A SEER Database Analysis

OBJECTIVE

Clinical prostate cancer (PCa) is rare in men aged <50 years (early-onset). A well-designed nomogram for prognosis prediction in patients with early-onset PCa has not been studied. Here, we tried to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in patients with early-onset PCa.

METHODS

The clinical variables of patients diagnosed with early-onset PCa between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups at a ratio of 7:3. Multivariate Cox regression analyses were used to select prognostic factors associated with OS or CSS, followed by the construction and validation of nomograms.

RESULTS

We enrolled 8259 patients with early-onset PCa. New nomograms were established and showed good discriminative abilities. Finally, ROC curve analysis demonstrated that these nomograms were superior to the TNM stage and Gleason score in predicting both OS and CSS for patients with early-onset PCa.

CONCLUSION

This is the first study to establish nomograms with effective and high accuracy for prognosis in patients with early-onset PCa.

Utility of Newly Proposed Grading System From International Association for the Study of Lung Cancer for Invasive Lung Adenocarcinoma

Introduction

The International Association for the Study of Lung Cancer proposed a new grading criteria for invasive adenocarcinoma. However, its utility has not been validated.

Methods

Patients who underwent complete resection of lung adenocarcinoma were included in this study. Then, they were divided into the following three groups on the basis of the criteria recently proposed by the International Association for the Study of Lung Cancer: grade 1, lepidic predominant tumor, with less than 20% of high-grade patterns; grade 2, acinar or papillary predominant tumor, with less than 20% of high-grade patterns; and grade 3, any tumor with greater than or equal to 20% of high-grade patterns.

Results

Recurrence-free survival (RFS) was significantly different among the proposed grades (p < 0.001). The RFS of patients upgrading from current grade 2 (papillary or acinar predominant tumor) to proposed grade 3 (5-y RFS, 65.2%) was significantly worse than that of patients with proposed grade 2 (77.1%, hazard ratio = 1.882, 95% confidence interval: 1.236–2.866) but not significantly different from that of patients with grade 3 in both the current (micropapillary or solid predominant tumor) and proposed criteria (53.2%, hazard ratio = 0.761, 95% confidence interval: 0.456–1.269). Among patients with pathologic stage 0 or I, RFS was well stratified by the new grading system (p < 0.001) but not among patients with stage II or III (p = 0.334). In the multivariable analysis, the new grading was not a predictive factor of RFS.

Conclusions

Although the proposed grading system well stratified RFS in patients with pathologic stage 0 or I lung adenocarcinoma, there is room for improvement.

A critical evaluation of visual proportion of Gleason 4 and maximum cancer core length quantified by histopathologists

Gleason score 7 prostate cancer with a higher proportion of pattern 4 (G4) has been linked to genomic heterogeneity and poorer patient outcome. The current assessment of G4 proportion uses estimation by a pathologist, with a higher proportion of G4 more likely to trigger additional imaging and treatment over active surveillance. This estimation method has been shown to have inter-observer variability. Fifteen patients with Prostate Grade Group (GG) 2 (Gleason 3 + 4) and fifteen patients with GG3 (Gleason 4 + 3) disease were selected from the PROMIS study with 192 haematoxylin and eosin-stained slides scanned. Two experienced uropathologists assessed the maximum cancer core length (MCCL) and G4 proportion using the current standard method (visual estimation) followed by detailed digital manual annotation of each G4 area and measurement of MCCL (planimetric estimation) using freely available software by the same two experts. We aimed to compare visual estimation of G4 and MCCL to a pathologist-driven digital measurement. We show that the visual and digital MCCL measurement differs up to 2 mm in 76.6% (23/30) with a high degree of agreement between the two measurements; Visual gave a median MCCL of 10 ± 2.70 mm (IQR 4, range 5–15 mm) compared to digital of 9.88 ± 3.09 mm (IQR 3.82, range 5.01–15.7 mm) (p = 0.64) The visual method for assessing G4 proportion over-estimates in all patients, compared to digital measurements [median 11.2% (IQR 38.75, range 4.7–17.9%) vs 30.4% (IQR 18.37, range 12.9–50.76%)]. The discordance was higher as the amount of G4 increased (Bias 18.71, CI 33.87–48.75, r 0.7, p < 0.0001). Further work on assessing actual G4 burden calibrated to clinical outcomes might lead to the use of differing G4 thresholds of significance if the visual estimation is used or by incorporating semi-automated methods for G4 burden measurement.

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

INTRODUCTION

A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma.

METHODS

A multi-institutional study involving multiple cohorts of invasive pulmonary adenocarcinomas was conducted. A cohort of 284 stage I pulmonary adenocarcinomas was used as a training set to identify histologic features associated with patient outcomes (recurrence-free survival [RFS] and overall survival [OS]). Receiver operating characteristic curve analysis was used to select the best model, which was validated (n = 212) and tested (n = 300, including stage I-III) in independent cohorts. Reproducibility of the model was assessed using kappa statistics.

RESULTS

The best model (area under the receiver operating characteristic curve [AUC] = 0.749 for RFS and 0.787 for OS) was composed of a combination of predominant plus high-grade histologic pattern with a cutoff of 20% for the latter. The model consists of the following: grade 1, lepidic predominant tumor; grade 2, acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and grade 3, any tumor with 20% or more of high-grade patterns (solid, micropapillary, or complex gland). Similar results were seen in the validation (AUC = 0.732 for RFS and 0.787 for OS) and test cohorts (AUC = 0.690 for RFS and 0.743 for OS), confirming the predictive value of the model. Interobserver reproducibility revealed good agreement (k = 0.617).

CONCLUSIONS

A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.

Methylation Markers in Prostate Biopsies Are Prognosticators for Late Biochemical Recurrence and Therapy after Surgery in Prostate Cancer Patients

After diagnosis of prostate cancer is confirmed by a positive biopsy, the tumor may be surgically removed via radical prostatectomy (RP). However, many prostate cancer patients experience biochemical recurrence after surgery and/or undergo salvage radiotherapy or hormone therapy. Timely treatment is required to prevent the spread of disease in these cases, and biopsy tissue may hold potential for disease prognostication before surgery is ever performed. We previously developed a prognostic multigene methylation panel in RP specimens, including APC, CRIP3, HOXD3, and TGFB2. In the current study, this panel was applied to a cohort of biopsy specimens (n = 86), which were assessed for DNA methylation using the real-time quantitative PCR-based multiplex MethyLight. The biopsy-based methylation panel is significantly associated with biochemical recurrence when combined with the current clinical parameter of prostate-specific antigen (PSA) levels at diagnosis and is able to prognosticate the initiation of salvage radiotherapy, where it outperforms PSA, and/or hormone therapy after RP. In addition, this methylation panel is significantly associated with late recurrence occurring within 5 and 7 years after surgery, when combined with PSA at diagnosis. Combining DNA methylation and clinicopathologic markers at the biopsy stage will not only increase their prognostic ability but will also ensure effective patient management.

An integrative DNA methylation model for improved prognostication of postsurgery recurrence and therapy in prostate cancer patients

PURPOSE

Patients with clinically localized, high-risk prostate cancer are often treated with surgery, but exhibit variable prognosis requiring long-term monitoring. An ongoing challenge for such patients is developing optimal strategies and biomarkers capable of differentiating between men at risk of early recurrence (<3 years) that will benefit from adjuvant therapies and men at risk of late recurrence (>5 years) who will benefit from long-term monitoring and/or salvage therapies.

PATIENTS AND METHODS

DNA methylation changes for 12 genes associated with disease progression were analyzed in 453 prostate tumors. A 4-gene prognostic model (4-G model) for biochemical recurrence (BCR) was derived utilizing LASSO from Cohort 1 (n = 254) and validated in Cohort 2 (n = 199). Subsequently, the 4-G model was evaluated for its association with salvage radiotherapy (RT) and/or hormone therapy, and the additive potential to CAPRA-S to develop an integrative gene model was assessed.

RESULTS

The 4-G model was significantly associated with BCR in both cohorts (chi-squared analysis P≤ 0.004) and specifically, with late recurrence at 5+ years (P < 0.001, Cohort 1; P= 0.028, Cohort 2). Multivariable Cox proportional regression analysis identified the 4-G model as significantly associated with salvage RT or hormone therapy in Cohort 1 (hazard ratio (HR) 1.64, 95% confidence interval (CI) 1.29-2.10, P< 0.001) and further validated in Cohort 2 (HR 1.63, 95% CI 1.18-2.25, P< 0.001). The integrative model outperformed prostate-specific antigen and the 4-G model alone for predicting BCR and was associated with patients who received hormone therapy 3+ years postsurgery.

CONCLUSIONS

We have identified and validated a novel integrative gene model as an independent prognosticator of BCR and demonstrated its association with late BCR. These patients require more long-term postsurgical monitoring and could be spared the comorbidities of adjuvant therapies.

RRBP1 is highly expressed in prostate cancer and correlates with prognosis

Objective: Recently, ribosome binding protein 1 (RRBP1) is reported to be involved in tumorigenesis. However, the expression and clinical significance of RRBP1 in prostate cancer (PCa) remains unknown. This study is aimed to investigate the expression and clinical significance of RRBP1 in PCa.

Materials and methods: RRBP1 expression was firstly detected in 6 cases of PCa and matched adjacent non-cancerous prostate tissues by reverse transcription-quantitative PCR (RT-qPCR) and Western blot. Then, RRBP1 expression was further detected in 127 cases of PCa and 40 cases of non-cancerous prostate tissues by immunohistochemistry (IHC). The relationship of RRBP1 with clinical-pathological characters and patients’ prognosis was analyzed in PCa.

Results: RT-qPCR and Western blot analysis showed that RRBP1 expression levels in PCa tissues were significantly higher compared with those in matched adjacent non-cancerous prostate tissues. IHC results shown that the high-expression rate of RRBP1 in PCa was 69.3%, which was significantly greater than those in non-cancerous prostate tissues (15.0%, P<0.001). RRBP1 expression was significantly associated with T stage, lymph node metastasis, PSA and Gleason score in PCa. Survival analysis indicated that patients with RRBP1 low-expression presented longer survival time compared with those with RRBP1 high-expression. Moreover, RRBP1 as well as T stage, lymph node metastasis and Gleason score could serve as independent prognostic factors in PCa.

Conclusion: RRBP1 is highly expressed in PCa and correlates with prognosis, which may serve as a potential biomarker in PCa.

Gleason grade grouping of prostate cancer is of prognostic value in Asian men

AIM

The International Society of Urological Pathology made recommendations for the use of Grade Groups (GG) originally described by Epstein and colleagues over Gleason score (GS) alone in 2014, which was subsequently adopted by the WHO classification in 2016. The majority of studies validating this revision have been in Caucasian populations. We therefore asked whether the new GG system was retrospectively associated with biochemical disease-free survival in a mixed-ethnicity cohort of Asian men.

METHODS

A total of 680 radical prostatectomies (RPs) from 2005 to 2014 were included. GS from initial biopsy and RP were compared and used to allocate cases to GG, defined as: 1 (GS≤6); 2 (GS 3+4=7); 3 (GS 4+3=7); 4 (GS 4+4=8/5+3=8/3+5=8) and 5 (GS 9-10). Biochemical recurrence was defined as two consecutive post-RP prostate-specific antigen (PSA) levels of >0.2 ng/mL after post-RP PSA reaching the nadir of <0.1 ng/mL.

RESULTS

Our data showed that Kaplan-Meier analysis revealed significant differences in biochemical recurrence within Gleason GG based on either biopsy or prostatectomy scoring. Multivariate analysis further confirmed that a higher GG was significantly associated with risk of biochemical recurrence. This GG system had a higher prognostic discrimination for both initial biopsy and RP than GS.

CONCLUSIONS

Our study validates the use of the revised and updated GG system in a mixed-ethnicity population of Asian men. Higher GG was significantly associated with increased risk of biochemical recurrence. We therefore recommend its use to inform clinical management for patients with prostate cancer.

Active surveillance as a therapeutic option for patients with low-risk prostate cancer according to the 2014 International Society of Urological Pathology grading system: a review

Extended prostate-specific antigen screening and the tightly focused execution of biopsies have resulted in an increased rate of detection, and thereby increased interventional treatment, of prostate cancer (PCa). The potential overdiagnosis and overtreatment of PCa patients have repeatedly been criticized in national and international literature. Controlled monitoring of patients in the setting of active surveillance (AS) can prevent overtreatment and the needless impairment of quality of life. The prerequisite for this treatment strategy is the diagnosis of low-grade/risk PCa. Since 2005, the modified Gleason grading system has been used for the histological assessment of PCa. In 2014, the International Society of Urological Pathology recommended a new prognostic grading system with five grades analogous to the modified Gleason score. This review discusses the importance of pathological histological analysis of PCa, particularly in the face of recent amendments, and sheds light on the significance of the new grading system for the diagnosis of low-grade/risk PCa with regard to the therapeutic option of AS.

[Prostate cancer. Part 2: Review of the various tumor grading systems over the years 1966-2015 and future perspectives of the new grading of the International Society of Urological Pathology (ISUP)]

The continued development of methods in needle biopsies and radical prostatectomy for treatment of prostate cancer has given special emphasis to the question of the prognostic relevance of the various systems of grading. The classical purely histological grading system of Gleason has been modified several times in the past decades and cleared the way for a new grading system by the prognostic grading of Epstein. Assessment of the old and also modified combined histological and cytological grading of Mostofi, the World health Organization (WHO) and the urologic-pathological working group of prostate cancer in connection with the Gleason grading (combined Gleason-Helpap grading), has led to considerably improved rates of concordance between biopsy and radical prostatectomy and to improved estimations of prognosis beside its contribution to the development of a more practicable grading system for clinical use.