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Cancers
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Innovative Approaches to the Liver Tumors: Molecular, Imaging, Surgical and...
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Innovative Approaches to the Liver Tumors: Molecular, Imaging, Surgical and Artificial Intelligence Applications in Clinical Practice

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5834

Special Issue Editors

Dr. Simone Famularo

E-Mail Website
Guest Editor
1. Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
2. Division of Hepatobiliary Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
3. Surgical Data Science Team, Institut de Recherche sur les Cancers de l’Appareil Digestif (IRCAD), 67000 Strasbourg, France
Interests: artificial intelligence in surgery; data science in surgery; hepatocellular carcinoma; colorectal liver metastases; advanced ultrasound guided liver surgery; circulating tumor cells in liver tumors; hyperspectral imaging; parenchymal sparing surgery
Dr. Francesco Ardito

E-Mail Website
Guest Editor
Hepatobiliary Surgery Unit, Foundation Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy
Interests: colorectal liver metastases; hepatocellular carcinoma; cholangiocarcinoma; minimally invasive liver surgery; robotic liver resection; parenchymal sparing liver resection; hepatic hypertrophy technique

Special Issue Information

Dear Colleagues,

Liver tumors can have a significant impact on overall health. The advancement of technology and medicine has enabled the development of innovative approaches to the treatment of liver tumors: from molecular biology to tailor the treatment allocation, through novel bio-imaging (such hyperspectral camera, functional MRI, perfusions etc) to estimate the remnant liver function or to drive precisely the surgical resection, to the novel applications of artificial intelligence. This special issue aims to bring together experts from a variety of fields to discuss and share their latest research findings on the innovative approaches to liver tumors and their relative surgical approach. By providing a comprehensive overview of the latest developments in this field, this special issue will provide valuable insights for researchers, clinicians, and other healthcare professionals interested in this topic.

We look forward to receiving your contributions!

Dr. Simone Famularo
Dr. Francesco Ardito
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • liver tumors
  • molecular biology
  • imaging
  • surgery
  • artificial intelligence
  • mini-invasive liver surgery
  • innovative approaches
  • hyperspectral imaging
  • surgical oncology

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Published Papers (3 papers)

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Research

12 pages, 7280 KiB  
Article
Robotic Complete ALPPS (rALPPS)—First German Experiences
by Jörg Arend, Mareike Franz, Alexander Rose, Christine March, Mirhasan Rahimli, Aristotelis Perrakis, Eric Lorenz and Roland Croner
Cancers 2024, 16(5), 1070; https://doi.org/10.3390/cancers16051070 - 6 Mar 2024
Viewed by 1301
Abstract
Background: ALPPS leads to fast and effective liver hypertrophy. This enables the resection of extended tumors. Conventional ALPPS is associated with high morbidity and mortality. MILS reduces morbidity and the robot adds technical features that make complex procedures safe. Material and Methods: The [...] Read more.
Background: ALPPS leads to fast and effective liver hypertrophy. This enables the resection of extended tumors. Conventional ALPPS is associated with high morbidity and mortality. MILS reduces morbidity and the robot adds technical features that make complex procedures safe. Material and Methods: The MD-MILS was screened for patients who underwent rALPPS. Demographic and perioperative data were evaluated retrospectively. Ninety days postoperative morbidity was scored according to the CD classification. The findings were compared with the literature. Results: Since November 2021, five patients have been identified. The mean age and BMI of the patients were 50.0 years and 22.7 kg/m2. In four cases, patients suffered from colorectal liver metastases and, in one case, intrahepatic cholangiocarcinoma. Prior to the first operation, the mean liver volume of the residual left liver was 380.9 mL with a FLR-BWR of 0.677%. Prior to the second operation, the mean volume of the residual liver was 529.8 mL with a FLR-BWR of 0.947%. This was an increase of 41.9% of the residual liver volume. The first and second operations were carried out within 17.8 days. The mean time of the first and second operations was 341.2 min and 440.6 min. The mean hospital stay was 27.2 days. Histopathology showed the largest tumor size of 39 mm in diameter with a mean amount of 4.7 tumors. The mean tumor-free margin was 12.3 mm. One complication CD > 3a occurred. No patient died during the 90-day follow up. Conclusion: In the first German series, we demonstrated that rALPPS can be carried out safely with reduced morbidity and mortality in selected patients. Full article
(This article belongs to the Special Issue Innovative Approaches to the Liver Tumors: Molecular, Imaging, Surgical and Artificial Intelligence Applications in Clinical Practice)
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15 pages, 1870 KiB  
Article
Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN
by Changzhe Jiao, Diane Ling, Shelly Bian, April Vassantachart, Karen Cheng, Shahil Mehta, Derrick Lock, Zhenyu Zhu, Mary Feng, Horatio Thomas, Jessica E. Scholey, Ke Sheng, Zhaoyang Fan and Wensha Yang
Cancers 2023, 15(14), 3544; https://doi.org/10.3390/cancers15143544 - 8 Jul 2023
Cited by 5 | Viewed by 1918
Abstract
Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 [...] Read more.
Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model’s effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment. Full article
(This article belongs to the Special Issue Innovative Approaches to the Liver Tumors: Molecular, Imaging, Surgical and Artificial Intelligence Applications in Clinical Practice)
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14 pages, 3585 KiB  
Article
Partial Hepatic Vein Occlusion and Venous Congestion in Liver Exploration Using a Hyperspectral Camera: A Proposal for Monitoring Intraoperative Liver Perfusion
by Simone Famularo, Elisa Bannone, Toby Collins, Elisa Reitano, Nariaki Okamoto, Kohei Mishima, Pietro Riva, Yu-Chieh Tsai, Richard Nkusi, Alexandre Hostettler, Jacques Marescaux, Eric Felli and Michele Diana
Cancers 2023, 15(8), 2397; https://doi.org/10.3390/cancers15082397 - 21 Apr 2023
Viewed by 1922
Abstract
Introduction. The changes occurring in the liver in cases of outflow deprivation have rarely been investigated, and no measurements of this phenomenon are available. This investigation explored outflow occlusion in a pig model using a hyperspectral camera. Methods. Six pigs were enrolled. The [...] Read more.
Introduction. The changes occurring in the liver in cases of outflow deprivation have rarely been investigated, and no measurements of this phenomenon are available. This investigation explored outflow occlusion in a pig model using a hyperspectral camera. Methods. Six pigs were enrolled. The right hepatic vein was clamped for 30 min. The oxygen saturation (StO2%), deoxygenated hemoglobin level (de-Hb), near-infrared perfusion (NIR), and total hemoglobin index (THI) were investigated at different time points in four perfused lobes using a hyperspectral camera measuring light absorbance between 500 nm and 995 nm. Differences among lobes at different time points were estimated by mixed-effect linear regression. Results. StO2% decreased over time in the right lateral lobe (RLL, totally occluded) when compared to the left lateral (LLL, outflow preserved) and the right medial (RML, partially occluded) lobes (p < 0.05). De-Hb significantly increased after clamping in RLL when compared to RML and LLL (p < 0.05). RML was further analyzed considering the right portion (totally occluded) and the left portion of the lobe (with an autonomous draining vein). StO2% decreased and de-Hb increased more smoothly when compared to the totally occluded RLL (p < 0.05). Conclusions. The variations of StO2% and deoxy-Hb could be considered good markers of venous liver congestion. Full article
(This article belongs to the Special Issue Innovative Approaches to the Liver Tumors: Molecular, Imaging, Surgical and Artificial Intelligence Applications in Clinical Practice)
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