The remarkable advances in our understanding of renal tumor pathogenesis, driven by the widespread application of molecular testing, are reflected in the latest 2022 World Health Organization classification. This updated classification categorizes renal cell carcinoma (RCC) into morphologically and molecularly defined RCCs. It includes updates to existing entities and introduces newly established and provisional entities. A standard macroscopic and microscopic evaluation is typically sufficient for diagnosing morphologically defined RCCs and serves as the initial step in the identification of molecularly defined entities. In cases where classification based solely on histologic examination is challenging, a limited panel of immunohistochemical stains can be employed to aid in the diagnosis, with molecular testing for validation if necessary. Therefore, this review explores the key clinical, pathological, and molecular features essential for classifying both the commonly encountered morphologically defined RCCs and the less common but clinically significant molecularly defined RCCs. The goal is to increase awareness of these RCC subtypes among clinicians and promote a deeper understanding of the pathological diagnostic process, ultimately improving patient care.

The remarkable advances in our understanding of renal tumor pathogenesis, driven by the widespread application of molecular testing, are reflected in the latest 2022 World Health Organization classification. This updated classification categorizes renal cell carcinoma (RCC) into morphologically and molecularly defined RCCs. It includes updates to existing entities and introduces newly established and provisional entities. A standard macroscopic and microscopic evaluation is typically sufficient for diagnosing morphologically defined RCCs and serves as the initial step in the identification of molecularly defined entities. In cases where classification based solely on histologic examination is challenging, a limited panel of immunohistochemical stains can be employed to aid in the diagnosis, with molecular testing for validation if necessary. Therefore, this review explores the key clinical, pathological, and molecular features essential for classifying both the commonly encountered morphologically defined RCCs and the less common but clinically significant molecularly defined RCCs. The goal is to increase awareness of these RCC subtypes among clinicians and promote a deeper understanding of the pathological diagnostic process, ultimately improving patient care.

The remarkable advances in our understanding of renal tumor pathogenesis, driven by the widespread application of molecular testing, are reflected in the latest 2022 World Health Organization classification. This updated classification categorizes renal cell carcinoma (RCC) into morphologically and molecularly defined RCCs. It includes updates to existing entities and introduces newly established and provisional entities. A standard macroscopic and microscopic evaluation is typically sufficient for diagnosing morphologically defined RCCs and serves as the initial step in the identification of molecularly defined entities. In cases where classification based solely on histologic examination is challenging, a limited panel of immunohistochemical stains can be employed to aid in the diagnosis, with molecular testing for validation if necessary. Therefore, this review explores the key clinical, pathological, and molecular features essential for classifying both the commonly encountered morphologically defined RCCs and the less common but clinically significant molecularly defined RCCs. The goal is to increase awareness of these RCC subtypes among clinicians and promote a deeper understanding of the pathological diagnostic process, ultimately improving patient care.

The remarkable advances in our understanding of renal tumor pathogenesis, driven by the widespread application of molecular testing, are reflected in the latest 2022 World Health Organization classification. This updated classification categorizes renal cell carcinoma (RCC) into morphologically and molecularly defined RCCs. It includes updates to existing entities and introduces newly established and provisional entities. A standard macroscopic and microscopic evaluation is typically sufficient for diagnosing morphologically defined RCCs and serves as the initial step in the identification of molecularly defined entities. In cases where classification based solely on histologic examination is challenging, a limited panel of immunohistochemical stains can be employed to aid in the diagnosis, with molecular testing for validation if necessary. Therefore, this review explores the key clinical, pathological, and molecular features essential for classifying both the commonly encountered morphologically defined RCCs and the less common but clinically significant molecularly defined RCCs. The goal is to increase awareness of these RCC subtypes among clinicians and promote a deeper understanding of the pathological diagnostic process, ultimately improving patient care.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.