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3. Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network

Author

Nan, Yang, Tang, Peng, Zhang, Guyue, Zeng, Caihong, Liu, Zhihong, Gao, Zhifan, Zhang, Heye, and Yang, Guang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value > 0.05) compared to the fully supervised U-Net., Comment: 13 pages, 8 figures, accepted by IEEE TMI

Published
2022

5. Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology

Author

Nan, Yang, Li, Fengyi, Tang, Peng, Zhang, Guyue, Zeng, Caihong, Xie, Guotong, Liu, Zhihong, and Yang, Guang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task. In this paper, we introduce a scheme to recognize fine-grained glomeruli lesions from whole slide images. First, a focal instance structural similarity loss is proposed to drive the model to locate all types of glomeruli precisely. Then an Uncertainty Aided Apportionment Network is designed to carry out the fine-grained visual classification without bounding-box annotations. This double branch-shaped structure extracts common features of the child class from the parent class and produces the uncertainty factor for reconstituting the training dataset. Results of slide-wise evaluation illustrate the effectiveness of the entire scheme, with an 8-22% improvement of the mean Average Precision compared with remarkable detection methods. The comprehensive results clearly demonstrate the effectiveness of the proposed method., Comment: 33 pages, 6 figures, accepted by the Pattern Recognition journal

Published
2022

8. Application of the updated International IgA Nephropathy Prediction Tool in children one or two years post-biopsy

Author

Zeng, Caihong, Su, Biage, Zhong, Xuhui, Nakanishi, Koichi, Zhai, Yihui, Urushihara, Maki, Hattori, Motoshi, Camassei, Francesca Diomedi, Barreca, Antonella, Robert, Thomas, Prikhodina, Larisa, Berg, Ulla, Topaloglu, Rezan, Mizerska-Wasiak, Malgorzata, Papagianni, Aikaterini, Bellur, Shubha S., Roberts, Ian, Barbour, Sean J., Coppo, Rosanna, Er, Lee, Russo, Maria Luisa, Liu, Zhi-Hong, Ding, Jie, Katafuchi, Ritsuko, Yoshikawa, Norishige, Xu, Hong, Kagami, Shoji, Yuzawa, Yukio, Emma, Francesco, Cambier, Alexandra, Peruzzi, Licia, Wyatt, Robert J., and Cattran, Daniel C.

Published
2024
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10. Histologic and Clinical Factors Associated with Kidney Outcomes in IgA Vasculitis Nephritis

Author

Barbour, Sean J., Coppo, Rosanna, Er, Lee, Pillebout, Evangeline, Russo, Maria Luisa, Alpers, Charles E., Fogo, Agnes B., Ferrario, Franco, Jennette, J. Charles, Roberts, Ian S.D., Cook, H. Terence, Ding, Jie, Su, Baige, Zhong, Xuhui, Fervenza, Fernando C., Zand, Ladan, Peruzzi, Licia, Lucchetti, Laura, Katafuchi, Ritsuko, Shima, Yuko, Yoshikawa, Norishige, Ichikawa, Daisuke, Suzuki, Yusuke, Murer, Luisa, Wyatt, Robert J., Park, Catherine, Nelson, Raoul D., Narus, JoAnn H., Wenderfer, Scott, Geetha, Duvuru, Daugas, Eric, Monteiro, Renato C., Nakatani, Shinya, Mastrangelo, Antonio, Nuutinen, Matti, Koskela, Mikael, Weber, Lutz T., Hackl, Agnes, Pohl, Martin, Pecoraro, Carmine, Tsuboi, Nobuo, Yokoo, Takashi, Takafumi, Ito, Fujimoto, Shouichi, Conti, Giovanni, Santoro, Domenico, Materassi, Marco, Zhang, Hong, Shi, Sufang, Liu, Zhi-Hong, Tesar, Vladimir, Maixnerova, Dita, Avila-Casado, Carmen, Bajema, Ingeborg, Barreca, Antonella, Becker, Jan U., Comstock, Jessica M., Cornea, Virgilius, Eldin, Karen, Hernandez, Loren Herrera, Hou, Jean, Joh, Kensuke, Lin, Mercury, Messias, Nidia, Muda, Andrea Onetti, Pagni, Fabio, Diomedi-Camassei, Francesca, Tokola, Heikki, DʼArmiento, Maria, Seidl, Maximilian, Rosenberg, Avi, Sannier, Aurélie, Soares, Maria Fernanda, Wang, Suxia, Zeng, Caihong, and Haas, Mark

Published
2024
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14. P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

Author

Wu, Bingzhe, Zhao, Shiwan, Sun, Guangyu, Zhang, Xiaolu, Su, Zhong, Zeng, Caihong, and Liu, Zhihong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, deep convolutional neural networks (CNNs) have achieved great success in pathological image classification. However, due to the limited number of labeled pathological images, there are still two challenges to be addressed: (1) overfitting: the performance of a CNN model is undermined by the overfitting due to its huge amounts of parameters and the insufficiency of labeled training data. (2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset. The smaller the dataset, the worse the privacy leakage. To tackle the above two challenges, we introduce a novel stochastic gradient descent (SGD) scheme, named patient privacy preserving SGD (P3SGD), which performs the model update of the SGD in the patient level via a large-step update built upon each patient's data. Specifically, to protect privacy and regularize the CNN model, we propose to inject the well-designed noise into the updates. Moreover, we equip our P3SGD with an elaborated strategy to adaptively control the scale of the injected noise. To validate the effectiveness of P3SGD, we perform extensive experiments on a real-world clinical dataset and quantitatively demonstrate the superior ability of P3SGD in reducing the risk of overfitting. We also provide a rigorous analysis of the privacy cost under differential privacy. Additionally, we find that the models trained with P3SGD are resistant to the model-inversion attack compared with those trained using non-private SGD., Comment: Accepted by CVPR 2019

Published
2019

17. G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Author

Wu, Bingzhe, Zhang, Xiaolu, Zhao, Shiwan, Xie, Lingxi, Zeng, Caihong, Liu, Zhihong, and Sun, Guangyu

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis. This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a two-stage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier. We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly., Comment: Accepted by AAAI 2019

Published
2018

20. Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients

Author

Liu, Xumeng, primary, Fang, Huiwen, additional, Liang, Dongmei, additional, Lei, Qunjuan, additional, Wang, Jiaping, additional, Xu, Feng, additional, Liang, Shaoshan, additional, Liang, Dandan, additional, Yang, Fan, additional, Li, Heng, additional, Chen, Jianghua, additional, Ni, Yuan, additional, Xie, Guotong, additional, and Zeng, Caihong, additional

Published
2024
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23. Updating the International IgA Nephropathy Prediction Tool for use in children

Author

Zeng, Caihong, Su, Biage, Zhong, Xuhui, Nakanishi, Koichi, Zhai, Yihui, Urushihara, Maki, Hattori, Motoshi, Camassei, Francesca Diomedi, Barreca, Antonella, Robert, Thomas, Prikhodina, Larisa, Berg, Ulla, Topaloglu, Rezan, Mizerska-Wasiak, Malgorzata, Papagianni, Aikaterini, Bellur, Shubha S., Roberts, Ian, Barbour, Sean J., Coppo, Rosanna, Er, Lee, Russo, Maria Luisa, Liu, Zhi-Hong, Ding, Jie, Katafuchi, Ritsuko, Yoshikawa, Norishige, Xu, Hong, Kagami, Shoji, Yuzawa, Yukio, Emma, Francesco, Cambier, Alexandra, Peruzzi, Licia, Wyatt, Robert J., and Cattran, Daniel C.

Published
2021
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26. Histologic and Clinical Factors Associated with Kidney Outcomes in IgA Vasculitis Nephritis

Author

Barbour, S, Coppo, R, Er, L, Pillebout, E, Russo, M, Alpers, C, Fogo, A, Ferrario, F, Jennette, J, Roberts, I, Cook, H, Ding, J, Su, B, Zhong, X, Fervenza, F, Zand, L, Peruzzi, L, Lucchetti, L, Katafuchi, R, Shima, Y, Yoshikawa, N, Ichikawa, D, Suzuki, Y, Murer, L, Wyatt, R, Nelson, R, Narus, J, Wenderfer, S, Geetha, D, Daugas, E, Monteiro, R, Nakatani, S, Mastrangelo, A, Nuutinen, M, Koskela, M, Weber, L, Hackl, A, Pohl, M, Pecoraro, C, Tsuboi, N, Yokoo, T, Takafumi, I, Fujimoto, S, Conti, G, Santoro, D, Materassi, M, Zhang, H, Shi, S, Liu, Z, Tesar, V, Maixnerova, D, Avila-Casado, C, Bajema, I, Barreca, A, Becker, J, Comstock, J, Cornea, V, Eldin, K, Hernandez, L, Hou, J, Joh, K, Lin, M, Messias, N, Muda, A, Pagni, F, Diomedi-Camassei, F, Tokola, H, D’Armiento, M, Seidl, M, Rosenberg, A, Sannier, A, Soares, M, Wang, S, Zeng, C, Haas, M, Barbour, Sean J., Coppo, Rosanna, Er, Lee, Pillebout, Evangeline, Russo, Maria Luisa, Alpers, Charles E., Fogo, Agnes B., Ferrario, Franco, Jennette, J. Charles, Roberts, Ian S. D., Cook, H. Terence, Ding, Jie, Su, Baige, Zhong, Xuhui, Fervenza, Fernando C., Zand, Ladan, Peruzzi, Licia, Lucchetti, Laura, Katafuchi, Ritsuko, Shima, Yuko, Yoshikawa, Norishige, Ichikawa, Daisuke, Suzuki, Yusuke, Murer, Luisa, Wyatt, Robert J., Nelson, Raoul D., Narus, JoAnn H., Wenderfer, Scott, Geetha, Duvuru, Daugas, Eric, Monteiro, Renato C., Nakatani, Shinya, Mastrangelo, Antonio, Nuutinen, Matti, Koskela, Mikael, Weber, Lutz T, Hackl, Agnes, Pohl, Martin, Pecoraro, Carmine, Tsuboi, Nobuo, Yokoo, Takashi, Takafumi, Ito, Fujimoto, Shouichi, Conti, Giovanni, Santoro, Domenico, Materassi, Marco, Zhang, Hong, Shi, Sufang, Liu, Zhi-Hong, Tesar, Vladimir, Maixnerova, Dita, Avila-Casado, Carmen, Bajema, Ingeborg, Barreca, Antonella, Becker, Jan U., Comstock, Jessica M., Cornea, Virgilius, Eldin, Karen, Hernandez, Loren Herrera, Hou, Jean, Joh, Kensuke, Lin, Mercury, Messias, Nidia, Muda, Andrea Onetti, Pagni, Fabio, Diomedi-Camassei, Francesca, Tokola, Heikki, D’Armiento, Maria, Seidl, Maximilian, Rosenberg, Avi, Sannier, Aurélie, Soares, Maria Fernanda, Wang, Suxia, Zeng, Caihong, Haas, Mark, Barbour, S, Coppo, R, Er, L, Pillebout, E, Russo, M, Alpers, C, Fogo, A, Ferrario, F, Jennette, J, Roberts, I, Cook, H, Ding, J, Su, B, Zhong, X, Fervenza, F, Zand, L, Peruzzi, L, Lucchetti, L, Katafuchi, R, Shima, Y, Yoshikawa, N, Ichikawa, D, Suzuki, Y, Murer, L, Wyatt, R, Nelson, R, Narus, J, Wenderfer, S, Geetha, D, Daugas, E, Monteiro, R, Nakatani, S, Mastrangelo, A, Nuutinen, M, Koskela, M, Weber, L, Hackl, A, Pohl, M, Pecoraro, C, Tsuboi, N, Yokoo, T, Takafumi, I, Fujimoto, S, Conti, G, Santoro, D, Materassi, M, Zhang, H, Shi, S, Liu, Z, Tesar, V, Maixnerova, D, Avila-Casado, C, Bajema, I, Barreca, A, Becker, J, Comstock, J, Cornea, V, Eldin, K, Hernandez, L, Hou, J, Joh, K, Lin, M, Messias, N, Muda, A, Pagni, F, Diomedi-Camassei, F, Tokola, H, D’Armiento, M, Seidl, M, Rosenberg, A, Sannier, A, Soares, M, Wang, S, Zeng, C, Haas, M, Barbour, Sean J., Coppo, Rosanna, Er, Lee, Pillebout, Evangeline, Russo, Maria Luisa, Alpers, Charles E., Fogo, Agnes B., Ferrario, Franco, Jennette, J. Charles, Roberts, Ian S. D., Cook, H. Terence, Ding, Jie, Su, Baige, Zhong, Xuhui, Fervenza, Fernando C., Zand, Ladan, Peruzzi, Licia, Lucchetti, Laura, Katafuchi, Ritsuko, Shima, Yuko, Yoshikawa, Norishige, Ichikawa, Daisuke, Suzuki, Yusuke, Murer, Luisa, Wyatt, Robert J., Nelson, Raoul D., Narus, JoAnn H., Wenderfer, Scott, Geetha, Duvuru, Daugas, Eric, Monteiro, Renato C., Nakatani, Shinya, Mastrangelo, Antonio, Nuutinen, Matti, Koskela, Mikael, Weber, Lutz T, Hackl, Agnes, Pohl, Martin, Pecoraro, Carmine, Tsuboi, Nobuo, Yokoo, Takashi, Takafumi, Ito, Fujimoto, Shouichi, Conti, Giovanni, Santoro, Domenico, Materassi, Marco, Zhang, Hong, Shi, Sufang, Liu, Zhi-Hong, Tesar, Vladimir, Maixnerova, Dita, Avila-Casado, Carmen, Bajema, Ingeborg, Barreca, Antonella, Becker, Jan U., Comstock, Jessica M., Cornea, Virgilius, Eldin, Karen, Hernandez, Loren Herrera, Hou, Jean, Joh, Kensuke, Lin, Mercury, Messias, Nidia, Muda, Andrea Onetti, Pagni, Fabio, Diomedi-Camassei, Francesca, Tokola, Heikki, D’Armiento, Maria, Seidl, Maximilian, Rosenberg, Avi, Sannier, Aurélie, Soares, Maria Fernanda, Wang, Suxia, Zeng, Caihong, and Haas, Mark

Abstract

Background: Nephritis is a common manifestation of IgA vasculitis and is morphologically indistinguishable from IgA nephropathy. While MEST-C scores are predictive of kidney outcomes in IgA nephropathy, their value in IgA vasculitis nephritis has not been investigated in large multiethnic cohorts. Methods: Biopsies from 262 children and 99 adults with IgA vasculitis nephritis (N=361) from 23 centers in North America, Europe and Asia were independently scored by 3 pathologists. MEST-C scores were assessed for correlation with eGFR/proteinuria at biopsy. Because most patients (N=309, 86%) received immunosuppression, risk factors for outcomes were evaluated in this group using Latent Class Mixed Models (LCMM) to identify classes of eGFR trajectories over a median follow-up of 2.7 years (IQR 1.2, 5.1). Clinical and histologic parameters associated with each class were determined using logistic regression. Results: M, E, T and C scores were correlated with either eGFR or proteinuria at biopsy. Two classes were identified by LCMM, one with initial improvement in eGFR followed by a late decline (Class 1, N=91) and another with stable eGFR (Class 2, N=218). Class 1 was associated with a higher risk of an established kidney outcome (time to >30% decline in eGFR or kidney failure, HR 5.84, 95%CI 2.37, 14.4). Among MEST-C scores, only E1 was associated with Class 1 by multivariable analysis. Other factors associated with Class 1 were age <18 years, male sex, lower eGFR at biopsy, and extrarenal non-cutaneous disease. Fibrous crescents without active changes were associated with Class 2. Conclusions: Kidney outcome in patients with biopsied IgA vasculitis nephritis treated with immunosuppression was determined by clinical risk factors, and endocapillary hypercellularity (E1) and fibrous crescents, which are features that are not part of the ISKDC classification..

Published
2024

30. Application of the updated International IgA Nephropathy Prediction Tool in children one or two years post-biopsy

Author

Barbour, Sean J., Coppo, Rosanna, Er, Lee, Russo, Maria Luisa, Liu, Zhi-Hong, Ding, Jie, Zhong, Xuhui, Katafuchi, Ritsuko, Yoshikawa, Norishige, Xu, Hong, Kagami, Shoji, Yuzawa, Yukio, Emma, Francesco, Cambier, Alexandra, Peruzzi, Licia, Wyatt, Robert J., Cattran, Daniel C., Zeng, Caihong, Su, Biage, Zhong, Xuhui, Nakanishi, Koichi, Zhai, Yihui, Urushihara, Maki, Hattori, Motoshi, Camassei, Francesca Diomedi, Barreca, Antonella, Robert, Thomas, Prikhodina, Larisa, Berg, Ulla, Topaloglu, Rezan, Mizerska-Wasiak, Malgorzata, Papagianni, Aikaterini, Bellur, Shubha S., and Roberts, Ian

Abstract

The pediatric International IgA Nephropathy (IgAN) Prediction Tool comprises two models with and without ethnicity and is the first method to predict the risk of a 30% decline in estimated glomerular filtration rate (eGFR) or kidney failure in children at the time of biopsy using clinical risk factors and Oxford MEST histology scores. However, it is unknown if the Prediction Tool can be applied after a period of observation post-biopsy. Using an international multi-ethnic cohort of 947 children with IgAN, 38% of whom were followed into adulthood, the Prediction Tool was updated for use one year after biopsy. Compared to the original pediatric Prediction Tool, the updated post-biopsy Prediction Tool had a better model fit with higher R2D(51%/50% vs 20%), significant increase in 4-year C-statistics (0.83 vs 0.73/0.69, ΔC 0.09 [95% confidence interval 0.07-0.10] and ΔC 0.14 [0.12-0.15]) and better 4-year calibration with lower integrated calibration indices (0.74/0.54 vs 2.45/1.01). Results were similar after internal validation and when the models were applied two years after biopsy. Trajectories of eGFR after a baseline one year post-biopsy were non-linear and those at higher predicted risk started with a lower eGFR and experienced a more rapid decline over time. In children, eGFR had a variable rate of increase until 15-18 years old and then decreased linearly with a more rapid decline in higher risk groups that was similar to young adults of comparable risk. Thus, the original pediatric Prediction Tool should be used in children at the time of biopsy, and the updated pediatric Prediction Tool should be used to re-evaluate risk one or two years after biopsy.

Published
2024
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35. Adverse Effects of Simulated Hyper- and Hypo-Phosphatemia on Endothelial Cell Function and Viability

Author

Peng, Ai, Wu, Tianfu, Zeng, Caihong, Rakheja, Dinesh, Zhu, Jiankun, Ye, Ting, Hutcheson, Jack, Vaziri, Nosratola D., Liu, Zhihong, Mohan, Chandra, and Zhou, Xin J.

Subjects
nitric-oxide synthase, phase protein microarrays, cardiovascular-disease, potential consequences, intracellular calcium, flow-cytometry, apoptosis, dysfunction, pathways, kinase
Abstract

BackgroundDysregulaiton of phosphate homeostasis as occurs in chronic kidney disease is associated with cardiovascular complications. It has been suggested that both hyperphosphatemia and hypophosphatemia can cause cardiovascular disease. The molecular mechanisms by which high or low serum phosphate levels adversely affect cardiovascular function are poorly understood. The purpose of this study was to explore the mechanisms of endothelial dysfunction in the presence of non-physiologic phosphate levels.Methodology/Principal FindingsWe studied the effects of simulated hyper- and hypophosphatemia in human umbilical vein endothelial cells in vitro. We found both simulated hyperphosphatemia and hypophosphatemia decrease eNOS expression and NO production. This was associated with reduced intracellular calcium, increased protein kinase C β2 (PKCβ2), reduced cell viability, and increased apoptosis. While simulated hyperphosphatemia was associated with decreased Akt/p-Akt, Bcl-xl/Bax ratios, NFkB-p65 and p-Erk abundance, simulated hypophosphatemia was associated with increased Akt/p-Akt and Bcl-xl/Bax ratios and p-Mek, p38, and p-p38 abundance.Conclusions/SignificanceThis is the first demonstration of endothelial dysfunction with hypophosphatemia. Our data suggests that both hyperphosphatemia and hypophosphatemia decrease eNOS activity via reduced intracellular calcium and increased PKCβ2. Hyperphosphatemia also appears to reduce eNOS transcription via reduced signaling through PI3K/Akt/NF-kB and MAPK/NF-kB pathways. On the other hand, hypophosphatemia appears to activate these pathways. Our data provides the basis for further studies to elucidate the relationship between altered phosphate homeostasis and cardiovascular disease. As a corollary, our data suggests that the level of phosphate in the culture media, if not in the physiologic range, may inadvertently affect experimental results.

Published
2011

44. Evaluation of Long-term Outcomes after Autologous Stem Cell Transplantation in Chinese Patients with AL Amyloidosis

Author

Xianghua Huang, Jinzhou Guo, Guisheng Ren, Liang Zhao, Wencui Chen, Weiwei Xu, Fujia Miao, Zeng Caihong, Yongchun Ge, and Zhihong Liu

Abstract

Treatment of AL amyloidosis is still a challenge, especially the long-term outcomes of patients with AL amyloidosis. Although autologous stem cell transplantation (ASCT) exhibited excellent efficacy, the long-term benefit needs to be evaluated. This study included a total of 240 patients with AL amyloidosis who underwent ASCT at our center and were followed up for more than 3 years. The median age for this cohort was 52 years, and 49% of them were male. The treatment-related mortality of this cohort was 1.7%. After a median follow-up of 66 months, the overall 1-, 3-, 5- and 10-year estimated overall survival (OS) rates were 94%, 90%, 79%, and 66%, respectively, and the median OS has not been reached. The 1-, 3-, 5- and 10-year progression-free survival (PFS) rates were 89%, 77%, 55%, and 33%, respectively, and the median PFS was 83 months. On multivariable analysis, the difference between involved and uninvolved free light chains (dFLC) > 50 mg/L, melphalan dose, hematologic CR/VGPR, and bortezomib induction therapy were independent predictors of survival. This study shows that ASCT has a very good long-term effect on AL amyloidosis and ASCT will remain an important option in eligible patients.

Published
2022

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